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Ladies and gentlemen, welcome back to the rounding the earth podcast.

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Rounding the earth is a multimedia education project based on the popular newsletter series

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published on Substack written by applied statistician and educator Matthew Crawford.

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Topics of discussion range from critical analysis of conventional wisdom to Bitcoin and everything

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in between and in particular the ongoing plandemonium.

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Our goal is a careful examination of important topics and perspectives shaping the world

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that too few people talk about.

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Subscribe to rounding the earth on local, Substack and Rumble to join a burgeoning research

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community and to help us unflatten the earth.

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My name is Liam Sturgis.

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I'm a musician, music producer and writer slash editor coming at you live from the thankfully

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sunny Vancouver, British Columbia, Canada, and I will be your host for today.

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But as always, I do not do it alone.

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Please allow me to introduce the author of rounding the earth and my cohost for this

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and most podcasts, Matthew Crawford.

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Good afternoon, Matthew.

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And you are on mute, sir.

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There we go.

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Thank you.

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That's me futzing with where I can watch the video in the chat as we go.

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I'm still not quite used to that.

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You know, all these, these whizzy techno gadgets that make a podcast happen, right?

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And I've had a lot of good conversations lately, talked to a lot of amazing people.

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But today's, today's guest, Josh, is one of the handful of people that I've counted on

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the most.

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Whereas, you know how sometimes you sift through information and you want to read things yourself.

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You don't want to promote anything that might not be tight.

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Well, Josh, Josh does tight work.

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He's been, you know, one of those people, you know, every time I go through what he's

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written, I understand it when I read it.

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Right.

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So it's one of those excellent qualities about somebody's capacity to handle information

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and explain it well.

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I'm really glad that he's here with us again.

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I am as well.

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And I'm very excited to let people know that we it's it's not just Josh.

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In fact, we've got a second guest today.

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We get a two on one.

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Yes.

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And I have been more and more aware of this gentleman's work, especially the ongoing

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partnership of work with Josh.

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And we're going to have the the duo of gentlemen sort of explain in more detail what they're

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doing.

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But I want to echo your excitement.

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And let's use this opportunity to reintroduce our friend, Josh Gutskow.

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Please tell me I got that right.

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And Pierre of Open Vate.

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How are you, gentlemen?

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Hi.

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Good evening, everyone.

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Thanks for the lovely introduction.

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Indeed.

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Yes.

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Pierre, I have to say real quick, I love that room that you're in, the architecture.

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That cozy home.

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It's an excellent retirement place from civilization indeed.

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Well, it's yeah, it's absolutely beautiful.

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Now, Pierre, can you confirm that I pronounced your handle correctly?

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Open Vate?

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Open Vate.

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Yeah, but you did.

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You did.

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Okay.

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Vaccine adverse event tracker.

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I just learned that today.

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And now so have the rest of us.

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So let's do this.

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Let's Josh, do you want to briefly reintroduce yourself to the audience who may not, some

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of whom may be watching you or running the earth for the first time?

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And then we'll have Pierre introduce himself to the audience as well.

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Okay, sure.

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My name is Josh Gutskow.

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Let's go Gutskow.

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So close.

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Next time.

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Next time.

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We'll do it in the green room beforehand.

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Okay, next time.

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My name is Josh.

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I'm a professor in sociology and criminology at the Hebrew University.

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I grew up in the US.

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Prior to the COVID fiasco, I wasn't doing anything related to this sort of thing.

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So I did do a postdoc at Harvard in health policy.

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And at some point I started turning my attention to issues related to the problems with the

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vaccines and that sort of thing.

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And then when the clinical trial data started coming out from Pfizer, I realized that I

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had the ability to transform the files from one format to another that people could use.

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I was doing that and then I kind of slowly, slowly, I was busy with other things.

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I didn't want to get into it.

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But then slowly, slowly, I realized I'm one of the few people I know that has kind of

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the ability to work with this type of data.

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So I started working on it.

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I was working on it kind of full time from like May for about six months.

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And then I got caught up with other things.

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And then recently, a few months ago, I connected with Pierre.

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I had kind of seen him prior to that.

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But I don't know what happened.

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We got connected and then we started a collaboration.

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And it's just been great because he's just extremely gifted and extremely creative and

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extremely knowledgeable about this data.

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It's actually been a delight to meet minds with somebody who is sort of into the data

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and understands the nuances of it.

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We can really talk about those things.

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So that's where we're at.

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And we've been kind of collaborating for a couple of months.

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I have to say that I haven't had time to do much of a lot of the analysis that we're going

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to be presenting.

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Pierre has really been the backbone of this and doing a lot of the heavy lifting on the

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data analysis.

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And there's a good advantage to that because Pierre uses Perl and all of his code is open

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source.

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He puts it out so anybody can go and download the data and do work, rerun his code and get

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the same results and see what he's doing.

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It's very, very transparent.

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Whereas the software that I have been using is proprietary, it costs money, and it's kind

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of hard to share my work.

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I'll stop there and let Pierre say more about himself.

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Yes.

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And Pierre, I just want to say I've been having a fantastic time following your updates on

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your substack.

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And I highly recommend everybody go and follow Pierre.

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And of course, Josh's substack is included in the description.

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We'll make sure to pull up any relevant articles as we go as well.

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So long story short, everyone should go subscribe to both if you have not yet.

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So Pierre, do you want to introduce yourself to us?

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Yes, I will briefly.

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Thanks to all for your kind words.

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And my own background is a bit atypical.

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I started by studying law.

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I mainly perfected my IT skills during that time.

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And I started in forensic and working with a big intelligence economic firm in Paris

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and learned a lot on big data analytics at this time, then moved to professional gambling.

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And I was mainly enjoying an interesting life when the COVID fiasco struck, to which my

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life took an unexpected turn.

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And I took some steps back as I wasn't able to appreciate the situation accurately.

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I was detecting a lot of propaganda, but had no access to accurate information.

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So I went in to work as an anonymous data, let's say, analyst, and put my skills to use

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where I could find, let's say, work worth doing.

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And I first started with adverse effects where at this time a lot of fake numbers were propagated

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in Europe to decredibilize the proposition movement.

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And from that moved, thanks to my friend Jeff Payne, to the Pfizer trial and was doing a

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lot of attempts to reproduce the efficacy analysis on the fragments of data we had at

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that time when I met Josh and shared the delight to meet someone who shared my perfectionism,

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if not pushed it higher.

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So where does your data come from?

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Is it pan-European or are you focusing on French data?

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On adverse effects, I worked on American and European data, the data vigilance and the

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virus and a lot of countries' scales.

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And on the Pfizer trial, it's mostly PHMPT, which is the data origin.

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Yeah, the Pfizer data dumps that everyone's familiar with but don't know how to read.

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Okay, very good.

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Well, just organizing is half the battle, right?

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Well, and I'll say I was surprised to see there are still dumps coming.

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You know, they've sort of exited the news cycle, but they came up last night in a meeting

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I was in and I went to check and indeed as of earlier this month, they're still dropping

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stuff.

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What is being disclosed at this point and is it relevant to the research you guys are

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doing?

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Oh yeah, absolutely.

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The stuff that was dumped this month was not really...

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We've been working a lot on the sort of subject level data, you know, and then they have kind

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of reports.

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So Pfizer basically gave the FDA the data and a bunch of reports about the data.

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And so we've been mainly trying to use the data itself, but this month what was released

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were a bunch of different reports.

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And one of the things that I was going to get to later, but I can bring it up now that

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Pierre had recently worked out was that...

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So you know, in mid-December they started giving people that were in the placebo group

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a real doses of the vaccine.

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Mid-December 2020.

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Mid-December 2020, yeah.

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Thanks for that clarification.

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So if you look at the number of adverse events that they reported, given the timeframe that

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they had, it's a far higher rate of adverse events than the original treatment group.

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Interesting.

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And he had found that out and then in these PDFs that were released, there was a memo

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from them that basically says that.

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And one of the weird things about it is they say as expected.

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They were expecting that.

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And so we're trying to figure out why in the heck were they expecting that.

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We don't know.

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One hypothesis is that they gave some priority to older people in giving them the real vaccine.

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And so they may have been expecting a higher rate of reporting of adverse events because

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these people are older and more frail and sicker.

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Because remember, adverse event doesn't necessarily mean it's caused by the vaccine.

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It's just like, sure, well, these people are more prone to morbidity in general and therefore

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we'd expect a higher rate.

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We don't know.

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Were the participants not randomized?

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Have they stated their method for sifting cohorts?

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In terms of the...

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In a randomized control trial, you would have some sort of randomization method to determine

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in person to push them into a cohort.

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So you make it sound like one hypothesis is that the cohorts were not of similar health

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status which would already indict the trials.

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No.

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Okay.

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No.

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What I mean is that when they...

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And I'll show you this.

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I have a graph that shows this.

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We should dive into this because we've got a lot of ground to cover.

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But basically when they went to give the placebo group, people in the placebo group, when they

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get to give them the real dose, they prioritized elderly people in the crossover group.

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But otherwise they're balanced.

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The placebo group and the treatment group were well balanced.

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Okay.

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Just throwing out one quick hypothesis is that shedding does matter.

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And that if they expected shedding to have affected the placebo group, then shedding

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may have primed them for a more harsh reaction later on.

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Okay.

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But why?

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I mean, they...

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I don't know.

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I mean, maybe, but it doesn't seem like the sort of thing that they would admit, they

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would say the quiet part out loud and say as expected.

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Yeah.

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It's still weird no matter how you slice it.

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And I'll tell you something.

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Okay.

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There's another...

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There's something that's recently come to light is that the vaccine trial was...

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The doses that were given to almost everybody in the trial were manufactured with a particular

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process, they call it process one, which was for these small batches.

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And they found they couldn't really scale it up, so they created a new manufacturing

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process.

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They call it process two, which they tested on 250 people in the entire trial.

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But by the time the placebo crossovers were ready to go, they already had...

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Process two doses, that's what they were sending out.

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That's what everybody in the world got.

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And I believe that that was also what the crossover placebo group got.

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And if you look, we have documents from the EMA and other things, there's a much lower

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purity of the process two batches.

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And so that may be why they were getting a higher rate of adverse events.

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We don't know.

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Okay.

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Well, because we've got a lot of ground to cover, as you say, do you want to dive into

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this presentation we got here?

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Comernati or Comernati.

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This is our sort of investigation.

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And here's a brief little...

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Oh, you know what?

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I got to go to this...

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Okay.

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So the overview is we're going to look at how long the trial actually lasted.

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This is something that people talk about, but we can look at with hard numbers.

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We want to... we're going to talk about this issue of missing subject or missing subject

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IDs that we found.

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We're going to look at the issue of trial blinding and unbalanced protocol deviations.

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Then we're going to look at imbalances.

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When I say imbalances, it means that the placebo group and the treatment group were not...

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They should be being treated and doing everything the same, but they're not.

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There's some imbalance between them.

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We're going to look at imbalances in testing rates, PCR testing rates.

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And then we're going to look at sort of this distinction between PCR positives and COVID

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cases.

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So how long did the trial last?

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Oh, so background here, you guys, we've already done this, so this is sort of irrelevant,

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but this is where we're getting our data.

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This is the documents that were released via a Freedom of Information Act request through

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this Act, the legal action of the public health and medical professionals for transparency.

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There's a lot of documents out there and there's a great little app at the URL there on the

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bottom right that was developed by some very talented people that you can go through and

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you can do word searches through all the PDF files and things like that.

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They've got a lot of other nice things at that website.

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So basically, beginning on December 14th, three days after the EUA was granted, they

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began unblinding the trial participants, the trial subjects.

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And you can kind of get a sense of the rate at which this happened.

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Now it's important to mention that the data that we have goes until March 13th.

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That's the cutoff, okay?

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And the application for the full approval of Comornati is based on data that goes through

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March 13th.

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And that's why that's the data cutoff.

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That's what they submitted to the FDA for this approval.

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So you can see that they're unblinding the trial subjects, placebo and treatment groups,

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fairly evenly throughout that period from December 14th to March 8th or March 13th.

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And then you can look and say, okay, well, from the time that people received their first

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dose until they were unblinded.

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So once you're unblinded, this kind of ends the trial for you anyway, because knowing

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what type of...

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If you've got the placebo or the treatment, it can affect the way you behave.

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And that's the whole reason that we try to do double blinding is because we don't want

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to affect the way that people will act or behave or create some kind of placebo effect

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or whatever.

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So basically, you get 137 days on average.

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The average number of days that anybody was in the trial from the first dose to unblinding

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was 137 days, so a little over four months, four and a half months.

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And that tracks them until the day they were actually unblinded.

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But if you look at how the unblinding went by age group, what you can see is that they

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weren't unblinding by age group equally.

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At the very beginning, there's a bit of a preference for 16 to 55s, but then they started

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unblinding the older age group at a higher rate.

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And so basically what happens, what this essentially means is that the trial really ended on December

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14th.

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Because once you create an imbalance in the participants who are in your study, in this

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case, they're creating an age imbalance.

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They're saying, okay, we're going to go and give these people the...

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We're going to unblind this group of people, this subgroup of people.

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If they had started unblinding randomly, okay, so then we could say that the trial really

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goes until people are un...

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Each individual is unblinded.

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So if you look at when people started, got the first dose until December 14th, the average

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number of days drops from 137 to 97.

304
00:20:54,360 --> 00:21:03,000
So you can basically say that the trial lasted basically three months, okay?

305
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Just a little over three months.

306
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And then that's it, right?

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And then the treatment arms and the ages get unbalanced and it's over.

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They also unblinded right at a time when you would introduce a new form of bias called

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risk adjusted person days, right?

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When you go into that period of high infectivity, and we saw this in one of the Israeli trials,

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I can't recall which one, maybe the BARDA trial.

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In one of the two retrospectives, they didn't adjust for the fact that there was a significantly

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larger number of people unvaccinated during the peak of COVID-19.

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And so you have much more risk per day in the unvaccinated group and that has to be

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00:22:00,960 --> 00:22:01,960
adjusted for.

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00:22:01,960 --> 00:22:07,720
And it looks like when they open up that unblinding process, I mean, that's the beginning of COVID

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season.

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00:22:08,720 --> 00:22:09,720
Right, right.

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Yeah, so there's a lot of room there for different types of bias to sneak in.

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00:22:20,320 --> 00:22:23,060
So now I'm going to get into the...

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00:22:23,060 --> 00:22:24,360
That was something that I had worked on.

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Now I'm going to get into some of the stuff that Pierre and I have been working on together.

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00:22:28,440 --> 00:22:38,840
And this is a synopsis of some of the major posts on his blog that I'm going to be distilling

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00:22:38,840 --> 00:22:44,080
down into the key points here.

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So one of the things that has puzzled us and been very troubling is what I call the curious

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case of missing subject IDs.

327
00:22:55,400 --> 00:23:03,440
So basically when a subject is...

328
00:23:03,440 --> 00:23:08,820
They have a screening process for the trial.

329
00:23:08,820 --> 00:23:14,740
The company that was hired to basically organize the trial is called ICON.

330
00:23:14,740 --> 00:23:19,420
And when people wanted to sign up for the trial, they would first contact ICON and they

331
00:23:19,420 --> 00:23:23,940
would have a screening, a pre-screening done through ICON.

332
00:23:23,940 --> 00:23:32,380
And then if they pass that pre-screening, then they would go into the actual site for

333
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another screening.

334
00:23:35,300 --> 00:23:41,860
And when they went into that site, essentially they signed the...

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00:23:41,860 --> 00:23:47,980
Once they signed the informed consent, which was at the very beginning or supposed to be

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00:23:47,980 --> 00:23:52,240
the very beginning, they would be assigned a subject ID number.

337
00:23:52,240 --> 00:24:00,240
And if they didn't pass the screening, that second stage of screening, if they didn't

338
00:24:00,240 --> 00:24:02,360
pass it, they would still have a subject ID.

339
00:24:02,360 --> 00:24:04,560
They were still in the data.

340
00:24:04,560 --> 00:24:06,880
It was just a screen failure.

341
00:24:06,880 --> 00:24:10,560
They were not randomized to placebo or treatment group.

342
00:24:10,560 --> 00:24:12,600
They're considered screen failure.

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00:24:12,600 --> 00:24:17,920
And so the data that they've released includes all of this group of people, those who are

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00:24:17,920 --> 00:24:24,140
randomized placebo treatment and those who were screened but failed the screening.

345
00:24:24,140 --> 00:24:32,660
And when a number, a subject ID number is assigned to somebody, it's done consecutively.

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00:24:32,660 --> 00:24:37,940
So at a given trial site, they start with the number 1001, and then the next person

347
00:24:37,940 --> 00:24:42,300
that comes in is 1002, and 1003, and 1004.

348
00:24:42,300 --> 00:24:48,420
The only way you wouldn't get assigned a subject ID number is if you didn't sign the informed

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consent.

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00:24:49,420 --> 00:24:55,600
So they're basically assigning numbers essentially the moment you walk in the door.

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So they're doing it consecutively.

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00:24:57,440 --> 00:25:02,220
But if you look in the data, you see that there are a lot of numbers that are skipped,

353
00:25:02,220 --> 00:25:04,360
a lot of subject ID numbers that are skipped.

354
00:25:04,360 --> 00:25:11,600
So you see for a given site, you'll see 1001, 1002, 1003, and then suddenly you see 1005.

355
00:25:11,600 --> 00:25:18,460
1006, and you're like, wait, what happened to 1004?

356
00:25:18,460 --> 00:25:23,440
And generally, this form of indexing, it's meant not to have gaps in the end for the

357
00:25:23,440 --> 00:25:24,440
most part.

358
00:25:24,440 --> 00:25:25,440
Exactly.

359
00:25:25,440 --> 00:25:29,760
And then you might find a reason to have deleted a record, do this or that.

360
00:25:29,760 --> 00:25:35,320
But if your procedures are good, you've created a process from beginning to end such that

361
00:25:35,320 --> 00:25:40,640
you do not have gaps and that if there's any data exclusion, you know why.

362
00:25:40,640 --> 00:25:41,720
The record is still there.

363
00:25:41,720 --> 00:25:43,960
You know why the data is being excluded.

364
00:25:43,960 --> 00:25:45,720
You've tracked everything.

365
00:25:45,720 --> 00:25:46,720
Exactly.

366
00:25:46,720 --> 00:25:47,720
Exactly.

367
00:25:47,720 --> 00:25:54,160
Now we do have, we have from Brooke Jackson, email or some kind of screenshot of a form

368
00:25:54,160 --> 00:26:01,240
that allowed the trial site to request the deletion of a subject ID.

369
00:26:01,240 --> 00:26:07,600
So we know that this could happen.

370
00:26:07,600 --> 00:26:14,320
But you're correct that it's not something that should happen very often.

371
00:26:14,320 --> 00:26:21,400
So we can accept that it may have occurred occasionally, but we have several anomalies

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00:26:21,400 --> 00:26:28,360
that make us suspicious that this wasn't just computer or user error in every case.

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00:26:28,360 --> 00:26:34,640
One of those anomalies is that we see that in many cases, there were several consecutive

374
00:26:34,640 --> 00:26:36,320
ID numbers that are skipped.

375
00:26:36,320 --> 00:26:42,840
So you have two numbers skipped, three numbers, all the way up to nine consecutive numbers

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00:26:42,840 --> 00:26:43,840
that are skipped.

377
00:26:43,840 --> 00:26:49,920
So it's hard to conceive of how that could have happened by computer error or subject

378
00:26:49,920 --> 00:26:52,760
error or human error.

379
00:26:52,760 --> 00:26:55,680
So that's one of the things about it.

380
00:26:55,680 --> 00:27:00,360
The other thing is that we see that, you know, we would also expect it to be kind of like

381
00:27:00,360 --> 00:27:03,480
randomly distributed across the sites, right?

382
00:27:03,480 --> 00:27:05,960
That people would be making mistakes kind of randomly.

383
00:27:05,960 --> 00:27:06,960
But we don't see that.

384
00:27:06,960 --> 00:27:13,240
We see them concentrated in a handful of sites and especially see it heavily concentrated

385
00:27:13,240 --> 00:27:18,400
at the Argentina site, Buenos Aires, right, where they have, you know, 12 percent of the

386
00:27:18,400 --> 00:27:24,160
trial subjects, but 37 percent of the skipped ID numbers.

387
00:27:24,160 --> 00:27:26,020
Now what is the concern here?

388
00:27:26,020 --> 00:27:28,120
What is the suspicion?

389
00:27:28,120 --> 00:27:38,640
The suspicion is that at some point they went back and deleted subjects whose results or

390
00:27:38,640 --> 00:27:42,980
something was inconvenient, was something that they wanted to try to hide.

391
00:27:42,980 --> 00:27:44,400
Maybe they died.

392
00:27:44,400 --> 00:27:46,720
Maybe they had a serious adverse event.

393
00:27:46,720 --> 00:27:47,720
We don't know.

394
00:27:47,720 --> 00:27:51,280
And again, it's a suspicion.

395
00:27:51,280 --> 00:28:01,560
We haven't been able to verify, you know, whether this could happen by some fluke of

396
00:28:01,560 --> 00:28:02,560
the software.

397
00:28:02,560 --> 00:28:05,280
We've contacted the FDA.

398
00:28:05,280 --> 00:28:08,120
We've contacted the ICON.

399
00:28:08,120 --> 00:28:11,840
And so far we haven't received an answer.

400
00:28:11,840 --> 00:28:14,240
If I could make two numerical observations there.

401
00:28:14,240 --> 00:28:19,040
OK, this looks like, correct me if I'm wrong, 301 missing subject IDs.

402
00:28:19,040 --> 00:28:20,960
Yes, 301 missing subject IDs.

403
00:28:20,960 --> 00:28:27,320
Which is in the ballpark of the entire effect size in terms of the total number of people

404
00:28:27,320 --> 00:28:31,220
between the two arms who got COVID in the arms.

405
00:28:31,220 --> 00:28:33,600
So that's one point that I want to make.

406
00:28:33,600 --> 00:28:42,280
The second one is 44,000 participants and there was a serious adverse event ratio of

407
00:28:42,280 --> 00:28:48,540
0.7 percent, if I recall correctly, and 0.7 percent of 44,000 is 308, which is suspiciously

408
00:28:48,540 --> 00:28:50,280
close to 301.

409
00:28:50,280 --> 00:28:52,000
Just throwing those numbers out there.

410
00:28:52,000 --> 00:28:53,000
OK, great.

411
00:28:53,000 --> 00:28:55,760
Well, here's some more suspicion to add to the file.

412
00:28:55,760 --> 00:28:59,960
OK, so we're looking at Argentina now.

413
00:28:59,960 --> 00:29:04,960
There's been a lot made of the Argentina site.

414
00:29:04,960 --> 00:29:14,960
But one of the things is that, OK, so the largest number, 111 of these 301 missed trial

415
00:29:14,960 --> 00:29:18,360
subjects were from the Argentina site.

416
00:29:18,360 --> 00:29:25,720
And on this day, when they had nine consecutive missing ID numbers, they had an additional

417
00:29:25,720 --> 00:29:26,720
eight.

418
00:29:26,720 --> 00:29:28,960
So they had 17 on one given day.

419
00:29:28,960 --> 00:29:32,880
This is August 21st, 2020.

420
00:29:32,880 --> 00:29:36,160
They had 17 missing subject ID numbers.

421
00:29:36,160 --> 00:29:38,680
I'm calling them missing because they should be there.

422
00:29:38,680 --> 00:29:39,680
They went missing.

423
00:29:39,680 --> 00:29:41,680
We don't know why.

424
00:29:41,680 --> 00:29:42,800
OK.

425
00:29:42,800 --> 00:29:46,160
Now here's something that's an interest.

426
00:29:46,160 --> 00:29:50,040
Now remember, we don't know when they were deleted from the data.

427
00:29:50,040 --> 00:29:53,800
We see that we see a snapshot of the data that was created on August.

428
00:29:53,800 --> 00:29:54,800
I'm sorry.

429
00:29:54,800 --> 00:30:01,480
On March 13th, 2021, these people could have been deleted on the first day, you know, August

430
00:30:01,480 --> 00:30:04,000
21st or any time in between.

431
00:30:04,000 --> 00:30:05,000
We don't know.

432
00:30:05,000 --> 00:30:07,040
All we see is this one snapshot.

433
00:30:07,040 --> 00:30:12,760
Now guess who was recruited to the trial on August 21st?

434
00:30:12,760 --> 00:30:14,400
Augusto Rue.

435
00:30:14,400 --> 00:30:15,480
OK.

436
00:30:15,480 --> 00:30:21,040
Now for people who aren't familiar with Augusto Rue's story, Augusto Rue is an Argentinian

437
00:30:21,040 --> 00:30:29,520
lawyer who was volunteered for the trial and became very ill just after his second dose

438
00:30:29,520 --> 00:30:31,960
and ended up in the hospital.

439
00:30:31,960 --> 00:30:34,400
When he went to the hospital, they took a PCR test.

440
00:30:34,400 --> 00:30:35,840
It was negative.

441
00:30:35,840 --> 00:30:39,520
He basically had pericarditis.

442
00:30:39,520 --> 00:30:46,040
When he reported it to the trial and the doctors at the hospital said it was related to the

443
00:30:46,040 --> 00:30:53,800
vaccine, and the nurses there said they've been seeing hundreds of people coming in from

444
00:30:53,800 --> 00:30:57,160
the trial with adverse events.

445
00:30:57,160 --> 00:31:06,080
You remember there were like over 5,000 subjects from Argentina in a very short space of time,

446
00:31:06,080 --> 00:31:12,720
4,000 in that space of time that were being vaccinated.

447
00:31:12,720 --> 00:31:15,320
So OK, sorry, I'm getting off track.

448
00:31:15,320 --> 00:31:16,560
So he had pericarditis.

449
00:31:16,560 --> 00:31:20,740
He calls the trial site to tell them what they have and they basically write it down

450
00:31:20,740 --> 00:31:26,440
as he has pneumonia.

451
00:31:26,440 --> 00:31:35,080
And then a few weeks later, the trial site in Argentina is contacted by the trial sponsor,

452
00:31:35,080 --> 00:31:48,280
BioNTech, asking them to change his adverse event from pneumonia to suspected COVID-19.

453
00:31:48,280 --> 00:31:53,440
And even though he had a PCR test that came out negative, they put it down as suspected

454
00:31:53,440 --> 00:31:54,440
COVID-19.

455
00:31:54,440 --> 00:32:02,520
Now here's an interesting loophole in the protocol is that they were not supposed to

456
00:32:02,520 --> 00:32:09,080
count cases of suspected COVID-19 as an adverse event.

457
00:32:09,080 --> 00:32:15,400
So if your adverse event was designated as a potential COVID-19 case, they didn't have

458
00:32:15,400 --> 00:32:18,880
to count it as an adverse event.

459
00:32:18,880 --> 00:32:26,840
Now in Augusto Rue's case, it is in the count of adverse events, but we don't know if other

460
00:32:26,840 --> 00:32:35,800
cases of suspected COVID-19 show a similar, you know, have if some of them were just a

461
00:32:35,800 --> 00:32:38,200
way to kind of cover up adverse events.

462
00:32:38,200 --> 00:32:47,240
And by the way, Pierre has tried every which possible way to recreate the numbers of suspected

463
00:32:47,240 --> 00:32:48,240
COVID-19 cases.

464
00:32:48,240 --> 00:32:54,280
You remember Peter Dochey wrote about this showing that if you look at suspected COVID-19

465
00:32:54,280 --> 00:32:58,760
cases, the efficacy is like 19% or something like that.

466
00:32:58,760 --> 00:33:03,800
He's done a heroic job of trying to replicate those numbers.

467
00:33:03,800 --> 00:33:06,240
He's come close, but not quite.

468
00:33:06,240 --> 00:33:12,840
So we still don't know how those numbers were exactly calculated.

469
00:33:12,840 --> 00:33:19,280
Now here's another thing, okay, another weird thing about missing subjects or related to

470
00:33:19,280 --> 00:33:20,580
the missing subjects problem.

471
00:33:20,580 --> 00:33:21,800
There is a file.

472
00:33:21,800 --> 00:33:26,400
So far we've only gotten the version that was submitted to the EUA, but I believe a

473
00:33:26,400 --> 00:33:29,640
similar version will be coming through the FDA pretty soon.

474
00:33:29,640 --> 00:33:32,880
This is a list of investigators and number of sites.

475
00:33:32,880 --> 00:33:37,720
Now the total number of subjects screened they have there, this is by the way includes

476
00:33:37,720 --> 00:33:42,400
the adolescent trial, 48,092.

477
00:33:42,400 --> 00:33:49,640
In the subject level data, the official data set for analyzing the efficacy that just came

478
00:33:49,640 --> 00:33:54,400
in March, by the way, they have 48,091.

479
00:33:54,400 --> 00:33:57,160
So they're off by one.

480
00:33:57,160 --> 00:34:04,800
But if you look on the lower right-hand side, it says subjects screened per site and subjects

481
00:34:04,800 --> 00:34:07,440
randomized or entered per site.

482
00:34:07,440 --> 00:34:17,840
So you see, now this is the page showing the Argentina site, 5,896 screened, 5,615 randomized.

483
00:34:17,840 --> 00:34:23,520
Now in their data, in the subject level data that we have, we know who was a screen failure

484
00:34:23,520 --> 00:34:25,280
and who was randomized, right?

485
00:34:25,280 --> 00:34:30,480
So we know for each site, we know the total number of people they screened, how many were

486
00:34:30,480 --> 00:34:31,640
randomized, right?

487
00:34:31,640 --> 00:34:33,360
How many weren't.

488
00:34:33,360 --> 00:34:35,720
The numbers don't line up.

489
00:34:35,720 --> 00:34:42,920
They line up for some of the sites, but for many of the sites, there are basically, if

490
00:34:42,920 --> 00:34:49,360
you go by the numbers in this document, there are far more people who were screened but

491
00:34:49,360 --> 00:34:56,200
not randomized according to this than in the actual data set that we have.

492
00:34:56,200 --> 00:35:05,160
So this is another huge anomaly between the number of, just basic numbers of the people

493
00:35:05,160 --> 00:35:11,240
that should be in the study just don't match up.

494
00:35:11,240 --> 00:35:15,520
By the way, feel free to stop me if you guys have any questions or something you want to

495
00:35:15,520 --> 00:35:16,520
add.

496
00:35:16,520 --> 00:35:17,960
Maybe just a very brief thought.

497
00:35:17,960 --> 00:35:24,840
One of my worries with the trials is that no one ever tested to see, we have a proxy

498
00:35:24,840 --> 00:35:29,000
test for disease, which is this PCR test.

499
00:35:29,000 --> 00:35:33,120
And the PCR test that was used, it's not even in use anymore, but one of the things that

500
00:35:33,120 --> 00:35:38,440
did not take place that ordinarily would have taken place during a longer trial procedure

501
00:35:38,440 --> 00:35:45,240
is a test to make sure that vaccination did not confound that proxy test.

502
00:35:45,240 --> 00:35:46,240
Okay, right, right.

503
00:35:46,240 --> 00:35:49,720
Okay, wait, there's going to be a perfect place later on to get into that discussion.

504
00:35:49,720 --> 00:35:50,720
Okay, okay.

505
00:35:50,720 --> 00:35:54,000
I do want to finish this sentence real quick.

506
00:35:54,000 --> 00:36:01,840
It is possible that that confounding depends on some factor that they could screen for.

507
00:36:01,840 --> 00:36:03,440
Yes.

508
00:36:03,440 --> 00:36:04,440
Yes.

509
00:36:04,440 --> 00:36:11,680
Well, and that assumes that they even would need to screen, right?

510
00:36:11,680 --> 00:36:16,840
They'd have to get around the blind in some circuitous fashion, right?

511
00:36:16,840 --> 00:36:18,880
That's what I understand you to be meaning.

512
00:36:18,880 --> 00:36:23,920
Well, I just mean maybe you can identify a portion of the population for whom the vaccine

513
00:36:23,920 --> 00:36:27,080
does or does not confound the PCR test.

514
00:36:27,080 --> 00:36:28,920
Oh, I see what you're saying.

515
00:36:28,920 --> 00:36:29,920
Okay.

516
00:36:29,920 --> 00:36:31,600
And scope the results that way.

517
00:36:31,600 --> 00:36:33,600
Right, yeah.

518
00:36:33,600 --> 00:36:40,360
One thing that, I mean, so one of the things I always like to point out here with this

519
00:36:40,360 --> 00:36:42,720
trial is that it's not a double blind trial.

520
00:36:42,720 --> 00:36:48,640
It was never billed as or presented as a double blind trial, okay?

521
00:36:48,640 --> 00:36:53,600
It was called what they call a observer blinded study.

522
00:36:53,600 --> 00:36:57,600
And if you go through the protocols in the different documents, you see kind of like

523
00:36:57,600 --> 00:37:03,160
this division of labor between who was supposed to be blinded and who was supposed to be blinded

524
00:37:03,160 --> 00:37:06,000
at the site level and at the study level.

525
00:37:06,000 --> 00:37:09,600
So you have things like, okay, you're going to have a blinded study coordinator, but the

526
00:37:09,600 --> 00:37:12,080
study manager is going to be unblinded.

527
00:37:12,080 --> 00:37:14,440
Like what is the division of labor there, right?

528
00:37:14,440 --> 00:37:19,640
And we know from, you know, from Brooke Jackson, the sites she worked at, there was just no

529
00:37:19,640 --> 00:37:21,840
blinding going on whatsoever, right?

530
00:37:21,840 --> 00:37:24,240
I mean, terrible protocols.

531
00:37:24,240 --> 00:37:27,920
We don't have any reason to suspect that the other sites were any better, right?

532
00:37:27,920 --> 00:37:33,600
But, and also there's just this, you know, how can you have a handful of people working

533
00:37:33,600 --> 00:37:36,920
side by side and some of them are blinded, some of them are unblinded.

534
00:37:36,920 --> 00:37:43,980
Now one of the, excuse me, one of the major justifications for this at the site level

535
00:37:43,980 --> 00:37:49,360
was that the vaccine requires a very special preparation, right?

536
00:37:49,360 --> 00:37:51,760
It's both storage and preparation.

537
00:37:51,760 --> 00:37:59,120
So there was somebody there and that presumably anybody doing the injections at least would

538
00:37:59,120 --> 00:38:07,640
be able to tell the difference between the study, you know, the investigational product

539
00:38:07,640 --> 00:38:10,080
versus the placebo.

540
00:38:10,080 --> 00:38:11,560
Okay.

541
00:38:11,560 --> 00:38:16,440
I don't know that that justifies the level of unblinding that went on at the site level,

542
00:38:16,440 --> 00:38:18,800
but that is their justification for that.

543
00:38:18,800 --> 00:38:19,800
Okay.

544
00:38:19,800 --> 00:38:24,240
And there is some anecdotal evidence that, you know, the subjects knew what they were

545
00:38:24,240 --> 00:38:25,240
getting, right?

546
00:38:25,240 --> 00:38:27,840
Isn't that, it's not rocket science.

547
00:38:27,840 --> 00:38:28,840
Okay.

548
00:38:28,840 --> 00:38:31,960
Now at the study level, there was also a lot of unblinding.

549
00:38:31,960 --> 00:38:36,840
One of the justifications for that was that they needed to do statistical analysis while

550
00:38:36,840 --> 00:38:39,440
the trial was ongoing.

551
00:38:39,440 --> 00:38:41,040
Okay.

552
00:38:41,040 --> 00:38:45,800
Because they were trying to do this at the speed of science.

553
00:38:45,800 --> 00:38:51,360
And another interesting justification, if you look at on the right-hand side study level,

554
00:38:51,360 --> 00:38:53,400
the medical monitor for adverse events.

555
00:38:53,400 --> 00:38:58,600
So they had an unblinded person that was monitoring the adverse events.

556
00:38:58,600 --> 00:39:03,720
Justification for that was they wanted to look for ADE, right?

557
00:39:03,720 --> 00:39:08,340
Antibody Dependent Enhancement where the vaccine would make the disease worse.

558
00:39:08,340 --> 00:39:10,000
So they were kind of on the lookout for this.

559
00:39:10,000 --> 00:39:11,080
They were worried about this.

560
00:39:11,080 --> 00:39:15,240
And I guess they thought if they started to see it, they would stop the study or something

561
00:39:15,240 --> 00:39:16,560
like that.

562
00:39:16,560 --> 00:39:24,160
But what this also does is it gives them free rein to go through the adverse events, call

563
00:39:24,160 --> 00:39:33,400
up the site and say, hey, can you reclassify that adverse event as suspected COVID-19?

564
00:39:33,400 --> 00:39:35,560
That sounds like something they did in Argentina.

565
00:39:35,560 --> 00:39:37,440
And it was something they did in Argentina.

566
00:39:37,440 --> 00:39:44,280
And we also know from Brooke Jackson that they asked the site that she worked at to do the

567
00:39:44,280 --> 00:39:45,280
same thing.

568
00:39:45,280 --> 00:39:48,040
So this was something that was definitely going on.

569
00:39:48,040 --> 00:39:57,240
And again, we don't know how many sort of adverse events were swept under the rug with

570
00:39:57,240 --> 00:39:58,720
this type of methodology.

571
00:39:58,720 --> 00:40:00,320
But it's very suspicious.

572
00:40:00,320 --> 00:40:07,240
Josh, just to interject, you just alluded to the notion that the recipients, the participants

573
00:40:07,240 --> 00:40:14,720
in the study, people having their arm injected with something may have known as a matter

574
00:40:14,720 --> 00:40:21,640
of fact kind of across the board whether they were getting the shot or the placebo.

575
00:40:21,640 --> 00:40:23,040
Can you just elaborate?

576
00:40:23,040 --> 00:40:24,340
Is that what you're saying?

577
00:40:24,340 --> 00:40:26,960
And if so, that has some interesting implications.

578
00:40:26,960 --> 00:40:27,960
Right.

579
00:40:27,960 --> 00:40:32,720
Well, definitely the person administering it knew.

580
00:40:32,720 --> 00:40:34,400
To what extent the people...

581
00:40:34,400 --> 00:40:40,160
So I've talked with Augusto Roo, he said a lot of people knew that they didn't get the...

582
00:40:40,160 --> 00:40:42,360
Felt that they got the placebo or they didn't get the placebo.

583
00:40:42,360 --> 00:40:44,560
And we're going to see a little bit of evidence of that too.

584
00:40:44,560 --> 00:40:48,720
I'm not saying everybody, every last person in the study knew that.

585
00:40:48,720 --> 00:40:54,760
But I think there's good reason to think that many people were aware of it.

586
00:40:54,760 --> 00:41:00,200
And part of why I pointed out is there was a video that it may have been JJ Cooey was

587
00:41:00,200 --> 00:41:05,760
going over a number of weeks or months ago where there was somebody right at the beginning,

588
00:41:05,760 --> 00:41:12,680
maybe it was December 2020, a high profile academic who was discussing his...

589
00:41:12,680 --> 00:41:17,320
He was promoting, get out the shot campaign.

590
00:41:17,320 --> 00:41:23,820
And he said that he was a part of one of the trials and he was not aware at that point

591
00:41:23,820 --> 00:41:27,200
if he had gotten the placebo or the actual product.

592
00:41:27,200 --> 00:41:31,420
And so this is why I bring this up is as we're starting to look back in other contexts and

593
00:41:31,420 --> 00:41:38,020
try to examine what people knew when and who had authority that was abused or not, so on

594
00:41:38,020 --> 00:41:43,080
so forth, it is interesting to add a layer of...

595
00:41:43,080 --> 00:41:48,840
Is there a layer of maybe that wasn't a totally truthful statement when he said one way or

596
00:41:48,840 --> 00:41:49,840
the other.

597
00:41:49,840 --> 00:41:50,840
Just one example.

598
00:41:50,840 --> 00:41:51,840
Right.

599
00:41:51,840 --> 00:41:52,840
And I mean, there's a difference.

600
00:41:52,840 --> 00:41:56,320
There's also, there could be shadings of knowing and thinking, right?

601
00:41:56,320 --> 00:41:58,720
I don't think they gave me the real thing.

602
00:41:58,720 --> 00:42:00,440
I think they gave me the placebo.

603
00:42:00,440 --> 00:42:02,140
Because I feel completely fine.

604
00:42:02,140 --> 00:42:04,140
So it can't have been the real thing.

605
00:42:04,140 --> 00:42:06,680
But now let's look at some evidence.

606
00:42:06,680 --> 00:42:11,000
The process that causes people to think through that is bad.

607
00:42:11,000 --> 00:42:12,000
Right.

608
00:42:12,000 --> 00:42:20,400
And also, again, the people conducting the study, many of them were aware of who was

609
00:42:20,400 --> 00:42:26,960
getting the placebo and who wasn't, which it just goes against the best practices that

610
00:42:26,960 --> 00:42:28,320
we have.

611
00:42:28,320 --> 00:42:31,040
It's hard to imagine that they couldn't have done a better job.

612
00:42:31,040 --> 00:42:34,520
First of all, the person preparing the dose didn't have to be the person administering

613
00:42:34,520 --> 00:42:35,520
it.

614
00:42:35,520 --> 00:42:38,720
So the person administering it didn't necessarily have to know that they may have been able

615
00:42:38,720 --> 00:42:41,800
to do a better job of making the placebo more like the product.

616
00:42:41,800 --> 00:42:42,800
I don't know.

617
00:42:42,800 --> 00:42:46,140
But let's look at some evidence suggesting that the blinding...

618
00:42:46,140 --> 00:42:49,800
This is what I call the Swiss cheese method of blinding.

619
00:42:49,800 --> 00:42:50,800
Okay.

620
00:42:50,800 --> 00:42:51,800
It's not double blind.

621
00:42:51,800 --> 00:42:53,160
It's not observer blinding.

622
00:42:53,160 --> 00:42:54,160
It's Swiss cheese.

623
00:42:54,160 --> 00:42:55,160
And let's look...

624
00:42:55,160 --> 00:42:58,680
So one of the ways we can look at that is we can look at imbalances across the treatment

625
00:42:58,680 --> 00:43:03,800
and placebo groups in certain protocol deviations.

626
00:43:03,800 --> 00:43:10,000
So whenever you deviate from protocol, a record is supposed to be made of it.

627
00:43:10,000 --> 00:43:13,400
And so we see some odds.

628
00:43:13,400 --> 00:43:19,480
Here's a handful of the protocol deviations where there's an imbalance across the arms.

629
00:43:19,480 --> 00:43:23,160
Each of these individually is statistically significant.

630
00:43:23,160 --> 00:43:26,600
I haven't done a test to look at...

631
00:43:26,600 --> 00:43:28,480
I forget what they call it.

632
00:43:28,480 --> 00:43:29,480
They're repeated tests.

633
00:43:29,480 --> 00:43:32,960
Do you know what that's called, Matthew?

634
00:43:32,960 --> 00:43:38,160
When you do a bunch of significance tests, you have to take into the fact that you've

635
00:43:38,160 --> 00:43:41,240
done a bunch and some of them didn't come out significant.

636
00:43:41,240 --> 00:43:42,240
Right.

637
00:43:42,240 --> 00:43:43,240
Right.

638
00:43:43,240 --> 00:43:48,320
We could flip coins all day and eventually we'll get a statistically significant at

639
00:43:48,320 --> 00:43:50,440
the end of the day, a group of tail result.

640
00:43:50,440 --> 00:43:56,560
So typically we correct by acting as if they were all part of the same pool.

641
00:43:56,560 --> 00:43:57,560
Yeah.

642
00:43:57,560 --> 00:44:02,640
So we have to go through and nail this down a little bit better.

643
00:44:02,640 --> 00:44:05,820
But just take this at face value for the moment.

644
00:44:05,820 --> 00:44:11,820
So we see, for example, an imbalance in the potential COVID illness visit not done when

645
00:44:11,820 --> 00:44:12,820
required.

646
00:44:12,820 --> 00:44:13,820
Meaning...

647
00:44:13,820 --> 00:44:14,820
What does this mean?

648
00:44:14,820 --> 00:44:21,160
It means that when people felt... when people had a symptom, when they weren't feeling ill,

649
00:44:21,160 --> 00:44:27,880
they were supposed to call in to the site and either do a visit or have some kind of

650
00:44:27,880 --> 00:44:33,560
phone call with the doctor about it.

651
00:44:33,560 --> 00:44:38,640
So what we're seeing here is that the people in the treatment group were less likely to

652
00:44:38,640 --> 00:44:41,600
do a visit when they were supposed to do a visit.

653
00:44:41,600 --> 00:44:42,960
And we don't know why that is.

654
00:44:42,960 --> 00:44:45,800
It may be that they knew that they had gotten the vaccine.

655
00:44:45,800 --> 00:44:49,280
And so they're like, well, I don't have COVID because I got the vaccine.

656
00:44:49,280 --> 00:44:50,720
I don't need to go in for a visit.

657
00:44:50,720 --> 00:44:51,720
That might be.

658
00:44:51,720 --> 00:44:52,720
I don't know.

659
00:44:52,720 --> 00:44:53,720
Right.

660
00:44:53,720 --> 00:44:54,720
We don't know.

661
00:44:54,720 --> 00:44:55,720
We have...

662
00:44:55,720 --> 00:44:56,720
Now here's something.

663
00:44:56,720 --> 00:45:00,320
The next one is something that actually works kind of in the favor of the placebo group,

664
00:45:00,320 --> 00:45:07,320
which is that they were collecting swabs from...

665
00:45:07,320 --> 00:45:08,320
I'm sorry.

666
00:45:08,320 --> 00:45:11,000
No, this doesn't work in favor of...

667
00:45:11,000 --> 00:45:14,320
Most swab collected at a visit where it's not required.

668
00:45:14,320 --> 00:45:22,160
So they were looking for infection from COVID at a higher rate in the placebo group than

669
00:45:22,160 --> 00:45:26,560
in the treatment group when it wasn't required.

670
00:45:26,560 --> 00:45:32,440
And then the next one is where it favors the placebo group.

671
00:45:32,440 --> 00:45:41,280
They're not collecting swabs at a higher rate in the placebo where it is required, et cetera,

672
00:45:41,280 --> 00:45:42,280
et cetera.

673
00:45:42,280 --> 00:45:43,720
Now here's, if you go down, look at this one.

674
00:45:43,720 --> 00:45:45,120
This is receipt of flu vaccine.

675
00:45:45,120 --> 00:45:46,120
No, I'm sorry.

676
00:45:46,120 --> 00:45:48,800
Receipt of other coronavirus vaccine.

677
00:45:48,800 --> 00:45:54,160
You can see that there were a lot more people in the placebo group who were out there trying

678
00:45:54,160 --> 00:45:57,040
to get another coronavirus vaccine.

679
00:45:57,040 --> 00:46:01,920
Now this was like in December, January when it had come out probably or something like

680
00:46:01,920 --> 00:46:05,840
that, or maybe there was another trial going on for another vaccine in the area.

681
00:46:05,840 --> 00:46:09,040
And they're like, well, I didn't get lucky with that one, so I'm going to go out and

682
00:46:09,040 --> 00:46:10,040
try another.

683
00:46:10,040 --> 00:46:13,600
But by that huge imbalance, you can see that there were a lot of people in the placebo

684
00:46:13,600 --> 00:46:16,280
group who felt strongly enough that they had gotten the placebo.

685
00:46:16,280 --> 00:46:18,600
They went out and got another vaccine.

686
00:46:18,600 --> 00:46:23,540
And then at the very bottom, you see urine pregnancy tests not performed.

687
00:46:23,540 --> 00:46:26,760
So it was more likely to happen in the placebo group.

688
00:46:26,760 --> 00:46:27,760
Right?

689
00:46:27,760 --> 00:46:31,480
Well, why would you not do a urine pregnancy test?

690
00:46:31,480 --> 00:46:33,320
Well, she got the placebo.

691
00:46:33,320 --> 00:46:35,840
Why do we need to do a pregnancy test?

692
00:46:35,840 --> 00:46:36,840
Right?

693
00:46:36,840 --> 00:46:48,400
Again, we don't know exactly what's behind these imbalances, but it's an indication

694
00:46:48,400 --> 00:46:50,800
that the blinding was far from watertight.

695
00:46:50,800 --> 00:46:51,800
Okay?

696
00:46:51,800 --> 00:46:58,880
Now, of course, one of the biggest imbalances is one that Matthew is one of the first, if

697
00:46:58,880 --> 00:47:00,680
not the first to point out.

698
00:47:00,680 --> 00:47:07,040
Now, this is from a table where they were looking at the efficacy.

699
00:47:07,040 --> 00:47:12,120
They're doing efficacy calculations, and they're taking people out of the efficacy

700
00:47:12,120 --> 00:47:13,120
calculation.

701
00:47:13,120 --> 00:47:15,440
They're excluding.

702
00:47:15,440 --> 00:47:20,280
These were people who were excluded from the efficacy calculations.

703
00:47:20,280 --> 00:47:22,080
It doesn't mean they were excluded from the trial.

704
00:47:22,080 --> 00:47:23,800
They weren't excluded from the trial.

705
00:47:23,800 --> 00:47:25,960
They just weren't included in this calculation.

706
00:47:25,960 --> 00:47:26,960
Okay?

707
00:47:26,960 --> 00:47:33,040
So, it's a huge imbalance where you have that left column of numbers is the treatment group,

708
00:47:33,040 --> 00:47:38,360
the middle column is the placebo group, and you see this big imbalance where you have

709
00:47:38,360 --> 00:47:40,800
a lot more people excluded.

710
00:47:40,800 --> 00:47:41,800
Okay?

711
00:47:41,800 --> 00:47:47,040
Now, this looks bad, but I got to tell you, I don't think there's anything to it.

712
00:47:47,040 --> 00:47:48,040
Okay?

713
00:47:48,040 --> 00:47:50,500
And I'll tell you why.

714
00:47:50,500 --> 00:47:59,520
First of all, we know who got a PCR test, who complained of symptoms, who got PCR tested,

715
00:47:59,520 --> 00:48:02,120
and what the PCR test results were.

716
00:48:02,120 --> 00:48:08,760
And there's no imbalance here between...if we look at this group of people who were excluded

717
00:48:08,760 --> 00:48:17,280
on these other important protocol deviations, we now know who those were.

718
00:48:17,280 --> 00:48:25,440
There's no imbalance in the number of people who got positive PCR test results.

719
00:48:25,440 --> 00:48:29,320
One interesting thing about all of these people, both in the placebo treatment group, is they

720
00:48:29,320 --> 00:48:37,720
all at some point reported symptoms, like a COVID symptom.

721
00:48:37,720 --> 00:48:42,560
All these people, this 371 or 372 number, you're saying that all those people reported

722
00:48:42,560 --> 00:48:44,200
COVID symptoms?

723
00:48:44,200 --> 00:48:48,480
One of the nine official COVID symptoms, yes.

724
00:48:48,480 --> 00:48:49,480
Okay.

725
00:48:49,480 --> 00:48:57,560
So I want to throw this one back into the mix because you say that it doesn't look like

726
00:48:57,560 --> 00:49:07,120
it might affect the efficacy calculations, but if the vaccination confounds the PCR test

727
00:49:07,120 --> 00:49:12,160
and you're saying that all of these people showed symptoms, then I would throw that back

728
00:49:12,160 --> 00:49:13,360
into yes.

729
00:49:13,360 --> 00:49:20,720
It absolutely does matter that I, as a statistician, could not sign off on an efficacy calculation

730
00:49:20,720 --> 00:49:23,720
with that kind of lopsided exclusion.

731
00:49:23,720 --> 00:49:24,720
Right.

732
00:49:24,720 --> 00:49:31,000
Now, what are these important protocol deviations?

733
00:49:31,000 --> 00:49:38,800
So we can kind of whittle down or look at the reasons that were given for these other

734
00:49:38,800 --> 00:49:41,760
important protocol deviations.

735
00:49:41,760 --> 00:49:48,160
And so when I have here 105 versus four, that's 105 people in the treatment group versus four

736
00:49:48,160 --> 00:49:49,560
in the placebo group.

737
00:49:49,560 --> 00:49:52,280
So there's a dosing or administration error.

738
00:49:52,280 --> 00:50:05,360
By the way, 52 of those 105 come from the Argentina site on October, I'm sorry, on August

739
00:50:05,360 --> 00:50:15,840
23rd, 2020, 52 people at the Argentina site were given a higher dose of the vaccine than

740
00:50:15,840 --> 00:50:17,000
they were supposed to.

741
00:50:17,000 --> 00:50:26,840
I think it was 56 or 62 milligrams instead of 30.

742
00:50:26,840 --> 00:50:34,400
And so they were told about this and they were, so they were told about this and then

743
00:50:34,400 --> 00:50:38,640
they were given a choice if they wanted to continue or not continue.

744
00:50:38,640 --> 00:50:42,480
Now okay, I didn't make this, I forgot to make this point earlier, so I'm going to come

745
00:50:42,480 --> 00:50:43,800
back to it now.

746
00:50:43,800 --> 00:50:45,000
That was August 23rd.

747
00:50:45,000 --> 00:50:52,960
Now remember August 21st, two days prior to that, is when Augusto Rue received his first

748
00:50:52,960 --> 00:50:59,520
dose and it's also the day when those 17, with those 17 missing subjects.

749
00:50:59,520 --> 00:51:00,520
Okay.

750
00:51:00,520 --> 00:51:05,800
So those missing subjects got their first dose on the same day as Augusto, which means

751
00:51:05,800 --> 00:51:10,100
they were supposed to come back three weeks later and get their second dose on the same

752
00:51:10,100 --> 00:51:12,760
day that Augusto got his second dose.

753
00:51:12,760 --> 00:51:18,760
Now what if Augusto had an adverse reaction, a severe adverse reaction because there was

754
00:51:18,760 --> 00:51:23,860
something wrong with the dose that he got on that second day?

755
00:51:23,860 --> 00:51:27,520
That means that all of those other people that came back on the second day or many of

756
00:51:27,520 --> 00:51:32,400
them could have also gotten something wrong with their dose, right?

757
00:51:32,400 --> 00:51:36,960
We saw it, we see here that there were 52 people on one day where they just totally

758
00:51:36,960 --> 00:51:39,180
screwed up their dosing.

759
00:51:39,180 --> 00:51:44,880
Maybe they did it or something else happened on that other day and they caused some severe

760
00:51:44,880 --> 00:51:52,360
adverse reactions in people to such an extent that they felt the need to erase those subjects

761
00:51:52,360 --> 00:51:53,360
from the trial.

762
00:51:53,360 --> 00:51:54,360
Okay.

763
00:51:54,360 --> 00:51:55,960
That's a suspicion.

764
00:51:55,960 --> 00:52:02,980
We don't know if that's what happened, but it's just weird that there are so many people

765
00:52:02,980 --> 00:52:10,320
missing a day where we know somebody had a very serious adverse reaction from the vaccine.

766
00:52:10,320 --> 00:52:18,320
In the old days, people did science with a notebook and that notebook never disappeared.

767
00:52:18,320 --> 00:52:23,040
That was the gospel of that experiment.

768
00:52:23,040 --> 00:52:24,520
Right.

769
00:52:24,520 --> 00:52:29,980
And here, we have essential... Pierre has been saying we need to find a way to do an

770
00:52:29,980 --> 00:52:36,760
audit of the actual database because all we see are extracts from the database.

771
00:52:36,760 --> 00:52:42,240
There are many different ways to manipulate the extracts from the database, which is basically

772
00:52:42,240 --> 00:52:44,440
all we have.

773
00:52:44,440 --> 00:52:45,440
Okay.

774
00:52:45,440 --> 00:52:52,480
So you see the IP, the investigational product was administered that was not deemed suitable

775
00:52:52,480 --> 00:52:54,080
for use by Allmac.

776
00:52:54,080 --> 00:53:04,760
Allmac was the company that was in charge of logistics, storage, and shipping of the

777
00:53:04,760 --> 00:53:05,760
vaccines.

778
00:53:05,760 --> 00:53:11,920
So if they had a shipment of vaccines and they saw that the temperature rose above a

779
00:53:11,920 --> 00:53:16,800
certain amount, then those doses were no longer deemed suitable.

780
00:53:16,800 --> 00:53:20,200
For example, that could be one reason why you get this.

781
00:53:20,200 --> 00:53:26,320
We have incorrect vaccine allocation assigned to the subject.

782
00:53:26,320 --> 00:53:32,380
And then the last one is this IP administered prior to Allmac authorizing it for use.

783
00:53:32,380 --> 00:53:33,800
We don't know what that means.

784
00:53:33,800 --> 00:53:43,360
We don't know why the vaccine wouldn't be... At what point is Allmac authorizing it?

785
00:53:43,360 --> 00:53:45,060
Why would they give it prior to that?

786
00:53:45,060 --> 00:53:48,760
We don't really know what that means exactly.

787
00:53:48,760 --> 00:53:56,440
Pierre thought maybe it was an indication about the process two versus process one.

788
00:53:56,440 --> 00:53:58,280
We don't know.

789
00:53:58,280 --> 00:54:04,980
One other just sort of interesting thing about that protocol deviation is that it's actually

790
00:54:04,980 --> 00:54:09,960
not coded in their data as an important protocol deviation.

791
00:54:09,960 --> 00:54:16,860
Now, these people were taken out of the efficacy analysis on the basis that they had some quote

792
00:54:16,860 --> 00:54:21,040
unquote other important protocol deviation.

793
00:54:21,040 --> 00:54:24,400
This was the only protocol deviation these people had.

794
00:54:24,400 --> 00:54:28,760
So they consider... Apparently, everybody who had this protocol deviation was taken

795
00:54:28,760 --> 00:54:30,320
out of the efficacy.

796
00:54:30,320 --> 00:54:34,280
So it could just be a coding error where they forgot to code this as important.

797
00:54:34,280 --> 00:54:35,800
I'm not really sure.

798
00:54:35,800 --> 00:54:43,720
But when you take all of these together, you basically end up with data that's no longer

799
00:54:43,720 --> 00:54:46,720
imbalanced.

800
00:54:46,720 --> 00:54:52,280
Now you could say, well, maybe there was some grand manipulation going on here where they

801
00:54:52,280 --> 00:54:57,880
were giving these people these protocol deviations to take them out and they were manipulating

802
00:54:57,880 --> 00:55:02,720
the results, the efficacy results on that basis.

803
00:55:02,720 --> 00:55:08,200
I don't think there's anything to this in a sense.

804
00:55:08,200 --> 00:55:10,680
This is not our smoking gun.

805
00:55:10,680 --> 00:55:15,280
This is not... That's my opinion.

806
00:55:15,280 --> 00:55:19,240
Okay.

807
00:55:19,240 --> 00:55:24,200
So now we're going to get to another issue which is related to the testing that they

808
00:55:24,200 --> 00:55:25,200
did.

809
00:55:25,200 --> 00:55:26,200
Okay.

810
00:55:26,200 --> 00:55:29,000
Josh, I'm sorry to cut you off before you move on to the next thought.

811
00:55:29,000 --> 00:55:33,600
Point of clarification, you might've said this, but Allmac, it's a drug discovery company

812
00:55:33,600 --> 00:55:35,200
that was running one of the trial sites.

813
00:55:35,200 --> 00:55:37,880
It's equivalent to Ventavia Research Group, right?

814
00:55:37,880 --> 00:55:38,960
No, no.

815
00:55:38,960 --> 00:55:49,400
They were running the logistics of shipping and storing the doses, the investigational

816
00:55:49,400 --> 00:55:50,840
product.

817
00:55:50,840 --> 00:55:56,240
Where in the hierarchy of the large tree of organizations involved in the project would

818
00:55:56,240 --> 00:55:59,680
they be compared to, for example, Ventavia?

819
00:55:59,680 --> 00:56:01,080
They would be above Ventavia.

820
00:56:01,080 --> 00:56:09,760
It would be like maybe contracted by ICON to run the logistics of the storage and shipping

821
00:56:09,760 --> 00:56:10,760
of the vaccine.

822
00:56:10,760 --> 00:56:11,760
Ventavia is a node.

823
00:56:11,760 --> 00:56:13,960
They're the network for distribution.

824
00:56:13,960 --> 00:56:15,520
Yeah, yeah, yeah.

825
00:56:15,520 --> 00:56:16,520
Exactly.

826
00:56:16,520 --> 00:56:17,520
That is helpful.

827
00:56:17,520 --> 00:56:18,520
Thank you.

828
00:56:18,520 --> 00:56:19,520
That's my understanding.

829
00:56:19,520 --> 00:56:22,520
I could be wrong.

830
00:56:22,520 --> 00:56:23,520
Okay.

831
00:56:23,520 --> 00:56:25,920
So now we're going to move on to the issue of testing.

832
00:56:25,920 --> 00:56:41,040
So the protocol says if you have one of these nine symptoms and a positive PCR test within

833
00:56:41,040 --> 00:56:49,520
say four days of symptom onset or resolution, then you would be counted as a symptomatic

834
00:56:49,520 --> 00:56:50,520
COVID case.

835
00:56:50,520 --> 00:56:51,680
And that's what they studied.

836
00:56:51,680 --> 00:56:54,280
That was their primary efficacy endpoint.

837
00:56:54,280 --> 00:56:58,960
They were only looking at these symptomatic COVID cases.

838
00:56:58,960 --> 00:57:06,400
Now this trial subjects were instructed to call in and report a wider range of symptoms

839
00:57:06,400 --> 00:57:14,280
because they also had the secondary list of COVID-19 related symptoms and stuff like that.

840
00:57:14,280 --> 00:57:17,600
But that's basically, this is how the testing work.

841
00:57:17,600 --> 00:57:24,040
So what they should have done really, if they wanted to do this very carefully, is they

842
00:57:24,040 --> 00:57:29,760
should have tested everybody at regular intervals, done a PCR regardless of symptoms.

843
00:57:29,760 --> 00:57:31,280
They didn't do that.

844
00:57:31,280 --> 00:57:37,640
You had to call in and tell them you were having a symptom and then they would instruct

845
00:57:37,640 --> 00:57:44,280
you either to come in and have a PCR test done or people were also given swabs to do

846
00:57:44,280 --> 00:57:46,280
at home.

847
00:57:46,280 --> 00:57:53,560
And all of the testing for the trial of whether or not you were PCR positive was done at one

848
00:57:53,560 --> 00:58:00,680
central laboratory, a Pfizer laboratory in New York.

849
00:58:00,680 --> 00:58:01,680
Okay.

850
00:58:01,680 --> 00:58:10,320
And this here's a little globe picture posted by Jickey Leakes showing that sort of distance

851
00:58:10,320 --> 00:58:16,080
from how absurd it is to think that you would need to send a swab all the way from Argentina

852
00:58:16,080 --> 00:58:22,280
to New York City for this rather than just have it tested on some local machine.

853
00:58:22,280 --> 00:58:29,680
But the fact that all of the testing that would allow for an efficacy analysis was being

854
00:58:29,680 --> 00:58:38,120
done at a central laboratory controlled by Pfizer raises one's eyebrows that they had

855
00:58:38,120 --> 00:58:45,680
levers there that they could manipulate potentially to make the trial outcome come out as they

856
00:58:45,680 --> 00:58:46,680
pleased.

857
00:58:46,680 --> 00:58:53,480
And for anybody listening, make no mistake, there are hundreds of PCR labs in Argentina.

858
00:58:53,480 --> 00:59:01,280
Right now, if you had a central PCR, if you had one of these central tests done, the results

859
00:59:01,280 --> 00:59:02,820
for weeks.

860
00:59:02,820 --> 00:59:08,400
So if you actually wanted to know if you had COVID, you would have to do a local test.

861
00:59:08,400 --> 00:59:14,040
In some of the trial sites, the trial site itself was offering to do a local test with

862
00:59:14,040 --> 00:59:21,560
a local machine that would be tested, run through a local machine, and you would get

863
00:59:21,560 --> 00:59:24,520
a result very quickly.

864
00:59:24,520 --> 00:59:28,360
In some cases, you had to go to your doctor to do that, or you had to go to a lab.

865
00:59:28,360 --> 00:59:33,360
You couldn't rely on the trial site to do that.

866
00:59:33,360 --> 00:59:40,120
So we have this distinction between the central tests controlled by Pfizer and the local tests.

867
00:59:40,120 --> 00:59:41,120
Okay.

868
00:59:41,120 --> 00:59:42,820
Now, I'm not going to get...

869
00:59:42,820 --> 00:59:48,720
There's a lot of nuances here, because in the protocol, they had three different PCR

870
00:59:48,720 --> 00:59:51,120
testing assays approved.

871
00:59:51,120 --> 01:00:01,520
They said, if in order for a PCR result to count as a COVID case, it has to be one of

872
01:00:01,520 --> 01:00:03,760
these three assays.

873
01:00:03,760 --> 01:00:05,720
Anything else won't do.

874
01:00:05,720 --> 01:00:13,320
And the central assay, they used one test as a CIFIID PCR test.

875
01:00:13,320 --> 01:00:19,360
Now, many of the local tests weren't done on any of those three assays, but that doesn't

876
01:00:19,360 --> 01:00:21,560
mean that their test results are invalid.

877
01:00:21,560 --> 01:00:23,720
So here's our distinction, central and local.

878
01:00:23,720 --> 01:00:28,240
So here's where we start to see a lot of shenanigans.

879
01:00:28,240 --> 01:00:32,360
So one of the things that we see...

880
01:00:32,360 --> 01:00:36,000
So one of the things that we see is that the treatment subjects were less likely to have

881
01:00:36,000 --> 01:00:40,000
local tests done.

882
01:00:40,000 --> 01:00:41,880
That's throughout the entire trial.

883
01:00:41,880 --> 01:00:46,480
Well, we don't know why that is, but it's a strong indication of a lack of blinding,

884
01:00:46,480 --> 01:00:47,480
right?

885
01:00:47,480 --> 01:00:57,320
Well, first of all, it could be that the trial site operators discourage people who...

886
01:00:57,320 --> 01:01:03,280
Disguise subjects from getting a local test done because they don't want a mismatch of

887
01:01:03,280 --> 01:01:07,520
results between what the local test is saying and what the central test is saying.

888
01:01:07,520 --> 01:01:08,920
Maybe they're trying to hide it.

889
01:01:08,920 --> 01:01:15,400
It could be simply the treatment subjects saying, well, I got the vaccine.

890
01:01:15,400 --> 01:01:19,640
I obviously don't have COVID, even though I have a symptom, but I'm not going to bother

891
01:01:19,640 --> 01:01:22,080
getting a local test because, hey, I'm vaccinated, right?

892
01:01:22,080 --> 01:01:24,060
I mean, it could be something like that.

893
01:01:24,060 --> 01:01:26,560
We don't know what this variation is.

894
01:01:26,560 --> 01:01:35,680
What's interesting though is that when we look at the data cutoff for the emergency

895
01:01:35,680 --> 01:01:40,800
youth authorization, the data cutoff for that was November 14th, okay?

896
01:01:40,800 --> 01:01:45,100
So they only use data up until November 14th, but if we look at the testing rates before

897
01:01:45,100 --> 01:01:50,920
and after that date, we see a big change.

898
01:01:50,920 --> 01:01:53,440
And we don't know what explains it.

899
01:01:53,440 --> 01:01:55,960
We see a big increase.

900
01:01:55,960 --> 01:02:04,560
So both placebo and treatment group subjects are sort of more likely to have a local test

901
01:02:04,560 --> 01:02:08,520
done after the EUA cutoff.

902
01:02:08,520 --> 01:02:14,760
That might be simply that there was a greater availability of tests at the local level at

903
01:02:14,760 --> 01:02:16,440
that time.

904
01:02:16,440 --> 01:02:17,440
We don't know.

905
01:02:17,440 --> 01:02:22,040
I don't think that's what it is because there was still plenty of tests to go around before

906
01:02:22,040 --> 01:02:23,320
that, but you never know.

907
01:02:23,320 --> 01:02:25,720
Now let's look at the central testing rates.

908
01:02:25,720 --> 01:02:31,960
If you look overall at the central testing rates, there's no difference between the rate

909
01:02:31,960 --> 01:02:37,640
at which the treatment group and the placebo groups were tested.

910
01:02:37,640 --> 01:02:46,640
But if you look at this EUA cutoff, you see this is very interesting where prior to the

911
01:02:46,640 --> 01:02:56,040
EUA data cutoff, the treatment group was less likely to get a central test.

912
01:02:56,040 --> 01:03:01,160
So I call in, I have a symptom, they're supposed to do a central test.

913
01:03:01,160 --> 01:03:06,240
Low and behold, we see that they weren't doing that equally for the treatment group and the

914
01:03:06,240 --> 01:03:07,240
placebo group.

915
01:03:07,240 --> 01:03:12,520
But of course, if no central test is done, then you're basically never going to get a

916
01:03:12,520 --> 01:03:15,120
positive PCR result.

917
01:03:15,120 --> 01:03:26,640
Now that difference is small, 84% versus 87%, but it is statistically significant.

918
01:03:26,640 --> 01:03:36,060
Moving forward, okay, so it isn't just the testing rate that this thing is going on.

919
01:03:36,060 --> 01:03:40,920
We can look at the results that they were getting from local tests that were done and

920
01:03:40,920 --> 01:03:45,400
compare those to the central tests that were done.

921
01:03:45,400 --> 01:03:50,320
Now remember that there were also a lot of other PCR tests that were being done according

922
01:03:50,320 --> 01:03:51,320
to protocol.

923
01:03:51,320 --> 01:03:57,360
They were supposed to get a PCR test done on the first day when they got their first

924
01:03:57,360 --> 01:03:58,360
dose.

925
01:03:58,360 --> 01:04:04,220
They were also supposed to have a PCR test done, regardless of symptoms, when they came

926
01:04:04,220 --> 01:04:06,120
in for their second dose.

927
01:04:06,120 --> 01:04:13,440
And even a month later when they came back, they were supposed to get a blood test done

928
01:04:13,440 --> 01:04:18,480
to test for their antibody levels that were often given a PCR test at that point.

929
01:04:18,480 --> 01:04:19,480
But if you compare-

930
01:04:19,480 --> 01:04:26,560
But tested before or after they took the dose?

931
01:04:26,560 --> 01:04:27,960
I don't know.

932
01:04:27,960 --> 01:04:33,200
I don't know.

933
01:04:33,200 --> 01:04:42,920
But basically what I'm showing you here are- okay, these are positive.

934
01:04:42,920 --> 01:04:49,480
If we take all of the PCR test results, local tests and central tests, put them together,

935
01:04:49,480 --> 01:04:54,040
what we see is an imbalance across the placebo group and the treatment group.

936
01:04:54,040 --> 01:05:00,480
This is a very busy table, so I'm just going to kind of give you a sense of- just go down

937
01:05:00,480 --> 01:05:03,960
to the very bottom row, this is symptom-driven tests.

938
01:05:03,960 --> 01:05:07,660
These are the protocol, per protocol tests.

939
01:05:07,660 --> 01:05:15,640
We can look at what percentage of people who had a positive PCR result, either central

940
01:05:15,640 --> 01:05:23,960
or local, that were counted as a COVID case for the efficacy analysis.

941
01:05:23,960 --> 01:05:33,440
In the placebo group, 79% of people with a positive PCR test result, symptom-driven positive

942
01:05:33,440 --> 01:05:42,400
PCR test result, were counted as cases compared to only 52% of the treatment.

943
01:05:42,400 --> 01:05:51,920
There's this huge imbalance across placebo and treatment group, right?

944
01:05:51,920 --> 01:05:58,800
Why is it that these people in the treatment group who have symptoms, they have a positive

945
01:05:58,800 --> 01:06:03,520
PCR test result, but they're not being counted as a COVID case?

946
01:06:03,520 --> 01:06:04,520
What gives?

947
01:06:04,520 --> 01:06:13,720
And the answer to that is that essentially the people in the treatment group who had

948
01:06:13,720 --> 01:06:23,560
a positive local test were more likely to have either a negative central test or a missing

949
01:06:23,560 --> 01:06:27,360
central test compared to the placebo group.

950
01:06:27,360 --> 01:06:29,440
A missing central test?

951
01:06:29,440 --> 01:06:31,760
Or a missing central test, yes.

952
01:06:31,760 --> 01:06:33,840
Well that's interesting.

953
01:06:33,840 --> 01:06:34,840
We don't know what that means.

954
01:06:34,840 --> 01:06:39,160
It could mean that they did it and it went missing, or it could just mean that they never

955
01:06:39,160 --> 01:06:40,260
did it, right?

956
01:06:40,260 --> 01:06:45,160
We don't know what stage of the process it was missing.

957
01:06:45,160 --> 01:06:46,640
It could be that it was never done.

958
01:06:46,640 --> 01:06:54,880
I just want to point out for the audience, that alone, if there's not a good reason for

959
01:06:54,880 --> 01:07:00,600
that bias, that would engineer 34% efficacy already.

960
01:07:00,600 --> 01:07:09,040
So if these factors add up amongst each other, that's basically a three to two ratio.

961
01:07:09,040 --> 01:07:10,440
Right.

962
01:07:10,440 --> 01:07:12,720
Right.

963
01:07:12,720 --> 01:07:15,200
And now I want to say something about these numbers quickly.

964
01:07:15,200 --> 01:07:17,400
These are numbers I calculated.

965
01:07:17,400 --> 01:07:19,400
Pierre has calculated the same thing.

966
01:07:19,400 --> 01:07:24,240
He didn't get exactly the same numbers here, and we haven't had a chance to kind of come

967
01:07:24,240 --> 01:07:31,880
together yet and work out an agreement on exactly what we're doing.

968
01:07:31,880 --> 01:07:34,120
But his findings were similar, right?

969
01:07:34,120 --> 01:07:42,760
So we know this was going on, these numbers might not be exact, but this is a major thing.

970
01:07:42,760 --> 01:07:50,400
And this I think comes back to your point, Matthew, about this idea of the test not being

971
01:07:50,400 --> 01:07:52,200
sensitive.

972
01:07:52,200 --> 01:07:59,160
So why is it that when the treatment group has a positive local test result, the central

973
01:07:59,160 --> 01:08:02,200
test is more likely to come back negative?

974
01:08:02,200 --> 01:08:10,480
Is that because of something related to the test, the central test not being sensitive

975
01:08:10,480 --> 01:08:16,040
to people who are vaccinated?

976
01:08:16,040 --> 01:08:26,560
Or were they pulling some other lever, like not doing the test as high a cycle count,

977
01:08:26,560 --> 01:08:28,240
as the placebo group?

978
01:08:28,240 --> 01:08:29,240
Right.

979
01:08:29,240 --> 01:08:31,120
Or maybe they were just fabricating it.

980
01:08:31,120 --> 01:08:32,960
We don't know why there was this.

981
01:08:32,960 --> 01:08:35,880
One way or another, I just want to point this out for people who have been following the

982
01:08:35,880 --> 01:08:41,160
story of how good are the PCR tests, right?

983
01:08:41,160 --> 01:08:47,520
Even if you want to call it a test, it's a proxy test at best.

984
01:08:47,520 --> 01:08:51,240
People like to say, oh no, these things are nearly 100% accurate.

985
01:08:51,240 --> 01:08:58,800
But here you've got substantial difference between a local test and a central test, right?

986
01:08:58,800 --> 01:09:05,560
If we can see that much variation in the differences, then these are not good tests for the disease.

987
01:09:05,560 --> 01:09:06,560
Right.

988
01:09:06,560 --> 01:09:13,200
But again, you're also assuming that the central tests were done properly and the results were

989
01:09:13,200 --> 01:09:18,280
recorded properly, which we can't necessarily assume.

990
01:09:18,280 --> 01:09:22,520
One last thing I want to point out on this slide is that these differences here are statistically

991
01:09:22,520 --> 01:09:26,200
significant.

992
01:09:26,200 --> 01:09:30,040
You don't get these results just by chance.

993
01:09:30,040 --> 01:09:32,080
And then so that's kind of it.

994
01:09:32,080 --> 01:09:36,440
Let me just summarize what we looked at.

995
01:09:36,440 --> 01:09:39,760
We have 301 quote unquote missing subject IDs.

996
01:09:39,760 --> 01:09:40,760
It's a mystery.

997
01:09:40,760 --> 01:09:43,100
We still don't know why they're missing.

998
01:09:43,100 --> 01:09:45,520
We do see large variation across sites.

999
01:09:45,520 --> 01:09:54,640
The missing IDs, especially the ones where you have things running in sequence, missing

1000
01:09:54,640 --> 01:10:00,720
in sequence are concentrated at a few sites, especially Argentina.

1001
01:10:00,720 --> 01:10:06,320
And then we have this discrepancy in the screened and randomized subjects in the report versus

1002
01:10:06,320 --> 01:10:09,020
the actual data.

1003
01:10:09,020 --> 01:10:13,520
We have treatment subjects being less likely to have local tests done.

1004
01:10:13,520 --> 01:10:17,960
Again, large vary unexpectedly large variation across sites.

1005
01:10:17,960 --> 01:10:20,840
It's not another way to say large variation.

1006
01:10:20,840 --> 01:10:21,840
I guess that's not right.

1007
01:10:21,840 --> 01:10:23,120
The right term.

1008
01:10:23,120 --> 01:10:25,560
It doesn't seem to be randomly distributed across the sites.

1009
01:10:25,560 --> 01:10:30,120
But that way it seems to be focused on a few sites.

1010
01:10:30,120 --> 01:10:36,680
With a change after the EUA cutoff, we have the treatment arm less likely to have a central

1011
01:10:36,680 --> 01:10:39,120
test before the EUA cutoff.

1012
01:10:39,120 --> 01:10:45,900
They were less likely than the SIBO group to have a central test done.

1013
01:10:45,900 --> 01:10:51,760
We have symptomatic treatment subjects with positive local tests were more likely to have

1014
01:10:51,760 --> 01:10:53,960
a negative or missing central test.

1015
01:10:53,960 --> 01:10:56,280
That was the thing that I was just showing.

1016
01:10:56,280 --> 01:10:59,640
And then the thing, and we go back to the very beginning before we started this, we

1017
01:10:59,640 --> 01:11:02,920
have this issue of process one versus process two, right?

1018
01:11:02,920 --> 01:11:09,960
They tested on process one and they gave everybody doses made from process two with a higher

1019
01:11:09,960 --> 01:11:18,760
adverse event rate among crossover placebos compared to the original treatment.

1020
01:11:18,760 --> 01:11:19,760
So yeah.

1021
01:11:19,760 --> 01:11:21,760
Wow.

1022
01:11:21,760 --> 01:11:23,960
Okay.

1023
01:11:23,960 --> 01:11:32,240
You know, I kind of get this sense there's two factions of people.

1024
01:11:32,240 --> 01:11:37,000
There are people who are so tired of hearing about Pfizer and their corrupt nonsense.

1025
01:11:37,000 --> 01:11:38,000
And we get it.

1026
01:11:38,000 --> 01:11:40,960
The shots are, best case scenario didn't do anything.

1027
01:11:40,960 --> 01:11:45,380
Worst case scenario are contributing to declining population, whatever.

1028
01:11:45,380 --> 01:11:50,240
Then you have the other side that finds this to be tremendously important to understand

1029
01:11:50,240 --> 01:11:56,520
as I put in the title, the nitty gritty of, you know, there are, for example, lawsuits

1030
01:11:56,520 --> 01:12:04,480
going forward in various jurisdictions around the world that require that in fact will rely

1031
01:12:04,480 --> 01:12:12,200
on some tiny detail that you guys are working on fleshing out and others like you.

1032
01:12:12,200 --> 01:12:16,920
So I fall into the camp of I want to understand exactly what happened.

1033
01:12:16,920 --> 01:12:20,840
I want to be able to explain it to everyone I know.

1034
01:12:20,840 --> 01:12:25,480
And the only way to do that is to go into, again, the nitty gritty.

1035
01:12:25,480 --> 01:12:31,840
So I want to start by saying thank you guys for doing this because I wouldn't even know

1036
01:12:31,840 --> 01:12:34,640
most people would not know where to start.

1037
01:12:34,640 --> 01:12:42,100
And this is something that does take credentials or long, well experienced period of having

1038
01:12:42,100 --> 01:12:43,560
done stuff like this before.

1039
01:12:43,560 --> 01:12:45,960
So I appreciate you very much.

1040
01:12:45,960 --> 01:12:49,960
That's what I want to say first.

1041
01:12:49,960 --> 01:12:51,020
I'm curious.

1042
01:12:51,020 --> 01:12:55,840
So you gave a summary of what you found so far and you kind of touched on what we don't

1043
01:12:55,840 --> 01:12:56,840
know.

1044
01:12:56,840 --> 01:13:01,880
What are the biggest things that we don't know yet that we clearly need to know?

1045
01:13:01,880 --> 01:13:07,760
That's a great question.

1046
01:13:07,760 --> 01:13:09,160
Not even entirely sure.

1047
01:13:09,160 --> 01:13:16,360
Well, one of the things is that we haven't been able to basically replicate anything

1048
01:13:16,360 --> 01:13:20,560
that Pfizer has reported in any of their memos.

1049
01:13:20,560 --> 01:13:25,540
And believe me, I've tried and Pierre has tried even harder than I have.

1050
01:13:25,540 --> 01:13:31,120
One of the problems that we have is that we only get the snapshot of the data as it was

1051
01:13:31,120 --> 01:13:32,800
on March 13th.

1052
01:13:32,800 --> 01:13:38,320
So if we want to recreate anything that was submitted for the emergency youth authorization,

1053
01:13:38,320 --> 01:13:43,680
for example, we don't have the data snapshot from November 14th.

1054
01:13:43,680 --> 01:13:48,360
We can kind of try to revert, go back and say, okay, we're going to stop the data.

1055
01:13:48,360 --> 01:13:52,380
We're going to like not consider any data after November 14th.

1056
01:13:52,380 --> 01:13:58,720
But even with that, we see things like people that I maybe Pierre can remind me, but you

1057
01:13:58,720 --> 01:14:03,440
see things like people who weren't excluded.

1058
01:14:03,440 --> 01:14:10,280
Then they were later excluded, even though the exclusion that they're the reason that

1059
01:14:10,280 --> 01:14:15,200
they're excluded happened at the very beginning of the study.

1060
01:14:15,200 --> 01:14:17,440
So why weren't they excluded by November 14th?

1061
01:14:17,440 --> 01:14:19,340
We don't know, but they weren't.

1062
01:14:19,340 --> 01:14:26,200
And so, you know, so you can and Pierre has come very, very, very close to replicating

1063
01:14:26,200 --> 01:14:28,320
the numbers on a number of different things.

1064
01:14:28,320 --> 01:14:31,480
But it just seems to be it's like always off.

1065
01:14:31,480 --> 01:14:37,680
And almost every time that we're trying to recreate something, we end up with more questions

1066
01:14:37,680 --> 01:14:38,680
that we've answered.

1067
01:14:38,680 --> 01:14:40,520
And many of those questions are nitty gritty.

1068
01:14:40,520 --> 01:14:46,080
I mean, if you guys think that we went into the nitty gritty in this presentation, believe

1069
01:14:46,080 --> 01:14:52,200
me, we didn't even get close to getting really nitty gritty.

1070
01:14:52,200 --> 01:14:58,800
But so, you know, so one thing is just like how, you know, hey, let's recreate one thing

1071
01:14:58,800 --> 01:15:03,320
that Pfizer reported to the FDA, you know, let's let's try to do that.

1072
01:15:03,320 --> 01:15:08,840
Now, Pierre has been in touch with Christine Cotton, who has a lot of experience actually

1073
01:15:08,840 --> 01:15:17,540
doing this and submitting clinical trial data to preparing, analyzing, submitting clinical

1074
01:15:17,540 --> 01:15:21,320
trial data to regulatory agencies.

1075
01:15:21,320 --> 01:15:31,400
She works with the statistical software that they use, Pfizer has released the syntax files

1076
01:15:31,400 --> 01:15:32,840
they used for that.

1077
01:15:32,840 --> 01:15:38,600
So if anybody is in a position to recreate what they've done, maybe Christine is because

1078
01:15:38,600 --> 01:15:41,680
she has the actual code that they used.

1079
01:15:41,680 --> 01:15:46,080
But Pierre has been in touch with her trying to recreate the code, you know, in a Perl-friendly

1080
01:15:46,080 --> 01:15:49,600
format and still, you know, they're still having trouble.

1081
01:15:49,600 --> 01:15:56,080
And by the way, there's a lot of work that Pierre has been doing with Christine or consulting

1082
01:15:56,080 --> 01:16:03,140
with Christine that's on his sub stack that we didn't even have a chance to go into.

1083
01:16:03,140 --> 01:16:09,160
So what are, Pierre, maybe you have a thought on what are some of the big, big picture?

1084
01:16:09,160 --> 01:16:14,160
In terms of what do we need, I would answer very simply a forensic copy of the database

1085
01:16:14,160 --> 01:16:21,840
server, that's the smallest thing we can have to reach confidence and clarity in this data.

1086
01:16:21,840 --> 01:16:29,600
And I'm not expecting that it will achieve confidence if we get our hand on it.

1087
01:16:29,600 --> 01:16:35,920
Briefly, we know more or less what the sub testing of the subjects has generated as bias

1088
01:16:35,920 --> 01:16:42,360
thanks to the visit-free antibodies testing, which were made and which are reflecting a

1089
01:16:42,360 --> 01:16:51,360
much higher part of infected people in the BNT group than what was accounted in the study.

1090
01:16:51,360 --> 01:17:00,440
Jekyllix made an article about that a while ago on the other expectation.

1091
01:17:00,440 --> 01:17:08,040
But back to the subject's direction, the fact that we strongly suspect that subjects have

1092
01:17:08,040 --> 01:17:14,720
disappeared at a given time of the study, the fact that we don't have data with accurate

1093
01:17:14,720 --> 01:17:21,720
timestamp allowing to reproduce the analysis per the ADLG, and the fact that they did themselves

1094
01:17:21,720 --> 01:17:28,120
mistakes in the data that we have documented, for example, not excluding at EUA time people

1095
01:17:28,120 --> 01:17:36,320
who hadn't received those one, introduce a number of potential bias in reproducing the

1096
01:17:36,320 --> 01:17:44,200
analysis that I doubt that everyone has ever done it, including at the regulator side,

1097
01:17:44,200 --> 01:17:48,640
which should have been done as far as I know.

1098
01:17:48,640 --> 01:17:56,600
First of all, the data is just so messy and complicated.

1099
01:17:56,600 --> 01:18:03,160
There's just no chance that people at the FDA did a real thorough job.

1100
01:18:03,160 --> 01:18:12,520
For example, there's a August statistical review memo from the FDA where they look at

1101
01:18:12,520 --> 01:18:19,760
the adverse event rate of the placebo group that crossed over and got the jabs.

1102
01:18:19,760 --> 01:18:29,440
They show that that rate is lower than in the original group, which contradicts the

1103
01:18:29,440 --> 01:18:31,520
memo that Pfizer submitted to them.

1104
01:18:31,520 --> 01:18:33,280
Actually, this might also be a memo.

1105
01:18:33,280 --> 01:18:39,120
I don't know, but they didn't control for observation time or exposure time, the person

1106
01:18:39,120 --> 01:18:40,120
years.

1107
01:18:40,120 --> 01:18:45,920
The placebo group was only under observation for a short period of time at the data cut

1108
01:18:45,920 --> 01:18:49,680
off compared to the original treatment group that was under observation for a much longer

1109
01:18:49,680 --> 01:18:50,680
period of time.

1110
01:18:50,680 --> 01:18:51,880
You have to control for that.

1111
01:18:51,880 --> 01:18:56,200
Now, if you don't control for that, then you don't see that it's a higher adverse event

1112
01:18:56,200 --> 01:18:59,160
rate, but when you do, you do see it.

1113
01:18:59,160 --> 01:19:04,200
In this statistical review memo that the FDA is going through very, very carefully and

1114
01:19:04,200 --> 01:19:11,960
whatever, they completely missed this basic fact, this basic issue.

1115
01:19:11,960 --> 01:19:16,120
Just to go back to what Pierre said, yes, this issue of the missing subjects I think

1116
01:19:16,120 --> 01:19:23,440
is really crucial and just a huge mystery.

1117
01:19:23,440 --> 01:19:26,680
I emailed Peter Marks about it.

1118
01:19:26,680 --> 01:19:33,200
He forwarded the email to somebody under him in the chain of command.

1119
01:19:33,200 --> 01:19:35,320
She emailed me and said, we'll look into it.

1120
01:19:35,320 --> 01:19:36,320
It'll take some time.

1121
01:19:36,320 --> 01:19:39,680
I emailed her back a week later saying, okay, great.

1122
01:19:39,680 --> 01:19:42,760
Do you have any idea how long it'll take?

1123
01:19:42,760 --> 01:19:45,360
I haven't heard back, and that was several weeks ago.

1124
01:19:45,360 --> 01:19:48,320
How long will it take for you to review the original trial report?

1125
01:19:48,320 --> 01:19:49,320
Yeah, right, exactly.

1126
01:19:49,320 --> 01:19:52,280
Or just to give us an idea of how this could happen.

1127
01:19:52,280 --> 01:19:55,440
How could you have all of these missing subjects?

1128
01:19:55,440 --> 01:20:04,520
All they could... I also submitted an inquiry to ICON, the company whose software was used

1129
01:20:04,520 --> 01:20:10,280
to run the trials to see if they could explain this glitch.

1130
01:20:10,280 --> 01:20:16,200
I didn't hear anything back from them at all.

1131
01:20:16,200 --> 01:20:19,240
It would be great to get some clarity on that.

1132
01:20:19,240 --> 01:20:23,000
I'd like to take a crack at summarizing all of this.

1133
01:20:23,000 --> 01:20:24,480
Okay, great.

1134
01:20:24,480 --> 01:20:25,480
Definitely.

1135
01:20:25,480 --> 01:20:32,000
Before this, I just want to point out something that several people that I've talked to who

1136
01:20:32,000 --> 01:20:35,720
have worked in BioTech or have worked in the pharmaceutical industry have told me, which

1137
01:20:35,720 --> 01:20:41,240
is that Pfizer is not much of a drug development company anymore.

1138
01:20:41,240 --> 01:20:46,760
It's almost a little bit weird for Pfizer to be controlling trials because more of their

1139
01:20:46,760 --> 01:20:52,240
business model over the past couple of decades has shifted toward being a supply chain management

1140
01:20:52,240 --> 01:20:57,960
company and a regulatory interface corporation.

1141
01:20:57,960 --> 01:21:02,400
Somebody can correct me if I'm wrong about that, but relative to the size and scale of

1142
01:21:02,400 --> 01:21:06,240
their business, they don't do a lot of trials.

1143
01:21:06,240 --> 01:21:13,720
When people have looked in, those trials have been less than scientific looking.

1144
01:21:13,720 --> 01:21:14,720
Right.

1145
01:21:14,720 --> 01:21:24,320
There is somebody, I think, who goes by the pseudonym, let's say Richard Kogan.

1146
01:21:24,320 --> 01:21:32,560
He's always tweeting people that it's not Pfizer, it's BioNTech.

1147
01:21:32,560 --> 01:21:34,040
It's not Pfizer, it's BioNTech.

1148
01:21:34,040 --> 01:21:35,040
It's not Pfizer, it's BioNTech.

1149
01:21:35,040 --> 01:21:36,040
It is true.

1150
01:21:36,040 --> 01:21:39,520
The study sponsors BioNTech.

1151
01:21:39,520 --> 01:21:43,160
What you're saying, it kind of makes sense that Pfizer would partner with a drug development

1152
01:21:43,160 --> 01:21:48,720
or the drug developer would partner with Pfizer to be their regulatory interface and to help

1153
01:21:48,720 --> 01:21:51,800
with the supply chain and the production and everything like that.

1154
01:21:51,800 --> 01:21:57,820
That level of compartmentalization, I think, encourages the compartmentalization of tasks

1155
01:21:57,820 --> 01:22:00,000
that allow for the potential manipulation of science.

1156
01:22:00,000 --> 01:22:04,200
But I'm going to summarize it like, I'm going to summarize all this like this.

1157
01:22:04,200 --> 01:22:09,000
If there is no notebook, did science take place?

1158
01:22:09,000 --> 01:22:13,140
If you can't reconstruct the process, if you can't follow the chain of data, if

1159
01:22:13,140 --> 01:22:19,880
people can't answer your questions 2.4 years later, if you can point to a part of the process

1160
01:22:19,880 --> 01:22:24,560
and ask a question and don't get a simple answer, then there is no universe in which

1161
01:22:24,560 --> 01:22:32,360
that trial report should have been accepted as conclusive on December 10th, 12th, 2020?

1162
01:22:32,360 --> 01:22:33,360
11th?

1163
01:22:33,360 --> 01:22:35,360
We'll take the average.

1164
01:22:35,360 --> 01:22:38,360
Yeah, there we go.

1165
01:22:38,360 --> 01:22:43,880
The entire notion of this being good science is total absurdity.

1166
01:22:43,880 --> 01:22:50,600
And thank you, thank both of you and the many other people, the Brooke-Jackson's, the people

1167
01:22:50,600 --> 01:22:53,520
who have looked at this on every level, the J.K.

1168
01:22:53,520 --> 01:23:02,240
Leakes, the mouse army, but everybody who has done careful work to shine a light in

1169
01:23:02,240 --> 01:23:06,440
so many corners that there really just isn't a good argument.

1170
01:23:06,440 --> 01:23:09,300
There is no trial notebook.

1171
01:23:09,300 --> 01:23:14,680
Science has not yet taken place, at least so far as has been communicated to anyone

1172
01:23:14,680 --> 01:23:18,360
in the public or any of us.

1173
01:23:18,360 --> 01:23:20,240
Now I'm curious.

1174
01:23:20,240 --> 01:23:28,920
I had a, I'm in multiple task force type groups trying to figure out, you know, public relations

1175
01:23:28,920 --> 01:23:35,600
type stuff, you know, to try to get people just in the general public aware of some things.

1176
01:23:35,600 --> 01:23:41,960
I'm in legal task groups trying to get the right information into the hands of the lawyers

1177
01:23:41,960 --> 01:23:46,080
in various contexts up here in Canada and further.

1178
01:23:46,080 --> 01:23:50,200
And first of all, there's someone I want to connect you guys with because you may be able

1179
01:23:50,200 --> 01:23:56,860
to help, but more generally, or rather on the bigger, more well-known case, you've alluded

1180
01:23:56,860 --> 01:24:04,500
to Brooke-Jackson and her experience at the Ventavia clinical trial site that has become

1181
01:24:04,500 --> 01:24:05,840
a very well-known story.

1182
01:24:05,840 --> 01:24:15,540
Now of course, in recent weeks, her, her key Tam case was dismissed.

1183
01:24:15,540 --> 01:24:19,560
And that doesn't mean it's the end of the road for that effort.

1184
01:24:19,560 --> 01:24:22,200
It does, it does complicate things a little bit.

1185
01:24:22,200 --> 01:24:28,240
There's some nuances to whether this was the better of two bad outcomes.

1186
01:24:28,240 --> 01:24:33,520
The alternative being that the Biden administration steps in and shuts it down themselves, which

1187
01:24:33,520 --> 01:24:36,280
was on the table.

1188
01:24:36,280 --> 01:24:43,400
My question I suppose is this all, I assume you guys have been working to some degree

1189
01:24:43,400 --> 01:24:49,360
with Brooke and with, with her counsel, with Warner Mendenhall, Robert Barnes.

1190
01:24:49,360 --> 01:24:53,760
Are you able to talk about any of that at all or, or, or confirm or deny?

1191
01:24:53,760 --> 01:24:54,760
You know what?

1192
01:24:54,760 --> 01:24:57,680
I actually had a conversation with Warner.

1193
01:24:57,680 --> 01:25:00,120
Pierre, I forgot to update you about this.

1194
01:25:00,120 --> 01:25:01,120
I'm sorry.

1195
01:25:01,120 --> 01:25:03,680
I had a conversation with him last week.

1196
01:25:03,680 --> 01:25:09,480
I went through some of these key, some of the, just very briefly, kind of like summarizing

1197
01:25:09,480 --> 01:25:10,800
some of these.

1198
01:25:10,800 --> 01:25:18,760
It's, it's, it's too, it seems to be maybe at least for him, what he's looking for just

1199
01:25:18,760 --> 01:25:22,760
too deep in the weeds at this point.

1200
01:25:22,760 --> 01:25:25,920
He says, I like, I like, I like simplicity.

1201
01:25:25,920 --> 01:25:33,880
Like I, you know, you want, these are, you know, each one of these things in and of itself

1202
01:25:33,880 --> 01:25:36,920
is not enough of a smoking gun, right?

1203
01:25:36,920 --> 01:25:41,360
And the, it doesn't get to a smoking gun by the sum of its parts, at least I think as

1204
01:25:41,360 --> 01:25:42,880
a, from a legal perspective.

1205
01:25:42,880 --> 01:25:43,880
No, I don't know.

1206
01:25:43,880 --> 01:25:44,880
I don't know.

1207
01:25:44,880 --> 01:25:47,500
But that, that seems to be his inclination.

1208
01:25:47,500 --> 01:25:55,080
If we were ever able to make some kind of strong, decisive claim about these missing

1209
01:25:55,080 --> 01:25:56,960
objects, I think that would be.

1210
01:25:56,960 --> 01:26:00,000
The smoking gun is that there's no science notebook.

1211
01:26:00,000 --> 01:26:01,000
Right.

1212
01:26:01,000 --> 01:26:04,840
But you can't sue, you know, I don't think they can, they're not going to be able to

1213
01:26:04,840 --> 01:26:06,840
make a legal claim on that basis.

1214
01:26:06,840 --> 01:26:08,080
I think.

1215
01:26:08,080 --> 01:26:10,920
But it, which is interesting.

1216
01:26:10,920 --> 01:26:16,040
I mean, it is, we should be able to make a legal claim on that basis.

1217
01:26:16,040 --> 01:26:19,480
I mean, the fact of the matter is that that's totally irresponsible science.

1218
01:26:19,480 --> 01:26:28,480
I mean, that's like, you know, if you have a restaurant, you don't have, I don't know,

1219
01:26:28,480 --> 01:26:30,520
someone come in and examine the kitchen, right?

1220
01:26:30,520 --> 01:26:31,520
Right.

1221
01:26:31,520 --> 01:26:35,880
There's no, there's no regulatory approval of, you know, we don't know that the kitchen

1222
01:26:35,880 --> 01:26:37,680
has been cleaned ever, right?

1223
01:26:37,680 --> 01:26:38,680
Right.

1224
01:26:38,680 --> 01:26:42,360
Or here's what you're taking, you're, you're, you're making the, that determination based

1225
01:26:42,360 --> 01:26:48,280
on pictures that the restaurant owner took of their kitchen and sent it into you and

1226
01:26:48,280 --> 01:26:50,680
said, Hey, look how clean our kitchen is.

1227
01:26:50,680 --> 01:26:51,680
Yeah.

1228
01:26:51,680 --> 01:26:56,040
Or, you know, you have, I don't know.

1229
01:26:56,040 --> 01:26:58,200
Twitter file style.

1230
01:26:58,200 --> 01:27:00,800
The notebook is the process.

1231
01:27:00,800 --> 01:27:07,080
And if there is no record of the process, then as far as, as far as the public should

1232
01:27:07,080 --> 01:27:16,520
be concerned, what you have is people promoting an image that a process has taken place.

1233
01:27:16,520 --> 01:27:21,800
And that is the expectation in all of history of science.

1234
01:27:21,800 --> 01:27:28,640
And so to, to put that forth and not have that record of the process, that, that should

1235
01:27:28,640 --> 01:27:33,860
be, and, and, and maybe, and maybe it's a matter of talking through this linguistically

1236
01:27:33,860 --> 01:27:40,960
until we can state it in a way that, that helps people understand it as best as possible.

1237
01:27:40,960 --> 01:27:48,200
But it, it, it feels like some sort of a sales fraud job, right?

1238
01:27:48,200 --> 01:27:51,200
It's not the same form of fraud that we're used to.

1239
01:27:51,200 --> 01:27:52,360
And that's part of why it's clever.

1240
01:27:52,360 --> 01:27:57,920
And I bet that this is something that has gone on within the pharmaceutical industry

1241
01:27:57,920 --> 01:27:58,920
in the past.

1242
01:27:58,920 --> 01:28:03,520
And I bet that it's, it's a perfected model because nobody has ever needed to look this

1243
01:28:03,520 --> 01:28:05,200
deep.

1244
01:28:05,200 --> 01:28:10,060
And if you have the FDA going, Oh, you know, this is all, this is okay with us because

1245
01:28:10,060 --> 01:28:12,000
they see a well-typed set trial report.

1246
01:28:12,000 --> 01:28:16,640
You know, a well-typed set trial report is not a science notebook.

1247
01:28:16,640 --> 01:28:17,640
Right.

1248
01:28:17,640 --> 01:28:18,640
Right.

1249
01:28:18,640 --> 01:28:23,760
But there's the illusion that these things are similar, if not the same, they are not

1250
01:28:23,760 --> 01:28:24,760
similar.

1251
01:28:24,760 --> 01:28:27,280
They are not the same.

1252
01:28:27,280 --> 01:28:30,160
We're coming up on the end of our time here and I don't want to keep you guys over.

1253
01:28:30,160 --> 01:28:32,840
I have two more questions I want to ask.

1254
01:28:32,840 --> 01:28:40,040
The first is it's interesting as we go through a presentation like yours, or we hear from

1255
01:28:40,040 --> 01:28:47,280
you for the first time names like Ventavia and Icon and for some even BioNTech.

1256
01:28:47,280 --> 01:28:50,640
Because we do think of this as the Pfizer vaccine.

1257
01:28:50,640 --> 01:28:56,240
Some people add the BioNTech at this point, but it occurs to me the, and, and this speaks

1258
01:28:56,240 --> 01:28:57,240
to it.

1259
01:28:57,240 --> 01:29:00,280
We were, I asked about Allmac and where they are in the system and what's their role in

1260
01:29:00,280 --> 01:29:02,180
the whole thing.

1261
01:29:02,180 --> 01:29:09,880
There are so many different organizations, both, you know, corporations, government agencies,

1262
01:29:09,880 --> 01:29:15,760
various types involved in this entire process, not to mention the principal investigators

1263
01:29:15,760 --> 01:29:16,880
at each of the sites.

1264
01:29:16,880 --> 01:29:21,480
And then you could go all the way down to their teams and lab assist, whatever.

1265
01:29:21,480 --> 01:29:27,240
But even just looking at the top level of just like, I'm wondering if anybody has ever

1266
01:29:27,240 --> 01:29:32,320
put together sort of a rough org chart just to be able to visualize.

1267
01:29:32,320 --> 01:29:35,680
And we're talking about this clinical trial as a whole.

1268
01:29:35,680 --> 01:29:36,980
This is what it looks like.

1269
01:29:36,980 --> 01:29:40,880
This is this map of the players involved.

1270
01:29:40,880 --> 01:29:42,600
Have you guys seen anything like that?

1271
01:29:42,600 --> 01:29:49,520
Or do you otherwise have any, any way to frame or point people towards some, some way to

1272
01:29:49,520 --> 01:29:54,320
be able to articulate and understand the scope of the organizations?

1273
01:29:54,320 --> 01:30:01,880
Because perhaps there are groups that could look into a given organization and more.

1274
01:30:01,880 --> 01:30:03,480
I don't know if anyone's done that.

1275
01:30:03,480 --> 01:30:04,480
It's Pierre.

1276
01:30:04,480 --> 01:30:06,640
He loves, he loves diagrams like that.

1277
01:30:06,640 --> 01:30:10,600
Indeed, I love diagram, but I haven't made this one so far.

1278
01:30:10,600 --> 01:30:14,160
So it will be one of the two things to be done quickly.

1279
01:30:14,160 --> 01:30:22,320
Another thing I have in mind is that we should do a map of the frauds observed in this trial,

1280
01:30:22,320 --> 01:30:26,560
meaning that on each site, it's not the same way to cheat.

1281
01:30:26,560 --> 01:30:35,360
And it may be amusing to have a visualization of this South African site at nine placebo

1282
01:30:35,360 --> 01:30:42,800
cases, but at three subjects disappeared and six subjects invalidated from efficacy, for

1283
01:30:42,800 --> 01:30:43,800
example.

1284
01:30:43,800 --> 01:30:44,800
Interesting.

1285
01:30:44,800 --> 01:30:51,280
I'm looking for ideas to, how to get people interested and how to get the argument simple

1286
01:30:51,280 --> 01:30:52,280
enough.

1287
01:30:52,280 --> 01:30:53,280
Right.

1288
01:30:53,280 --> 01:30:56,640
That's, that's the problem is making, keeping it, keeping it simple.

1289
01:30:56,640 --> 01:30:57,640
Okay.

1290
01:30:57,640 --> 01:31:02,560
Well, my, my last question is, um, what, what do people have wrong?

1291
01:31:02,560 --> 01:31:05,840
And maybe there's a horrible question to end with, cause this could, I'm sure go on for

1292
01:31:05,840 --> 01:31:10,700
a long time, but what, what, what do people believe that is incorrect or what is something

1293
01:31:10,700 --> 01:31:18,480
that is like a pervasive misunderstanding in our circles in general about the trial

1294
01:31:18,480 --> 01:31:20,440
or about what you've just gone through?

1295
01:31:20,440 --> 01:31:23,240
What's something people believe that isn't true?

1296
01:31:23,240 --> 01:31:24,720
Okay.

1297
01:31:24,720 --> 01:31:29,900
So one that I, that, that comes up again and again, and, and I, I believe that they've

1298
01:31:29,900 --> 01:31:39,400
actually corrected it, but, but, um, uh, Daily Clout and Naomi Wolf's organization for a

1299
01:31:39,400 --> 01:31:45,440
very long time, Naomi Wolf was quoting, um, miscarriage figures.

1300
01:31:45,440 --> 01:31:50,960
And at some point she was saying that these were people in the clinical trial that there,

1301
01:31:50,960 --> 01:31:57,240
there were these pregnant women in the clinical trial and high rate of them had miscarriages,

1302
01:31:57,240 --> 01:32:02,100
which there was a confusion there because they were taking a document that was based

1303
01:32:02,100 --> 01:32:06,920
on post-marketing adverse event reports.

1304
01:32:06,920 --> 01:32:12,800
Post-marketing means after they started deploying the vaccine, um, to millions of people, they

1305
01:32:12,800 --> 01:32:18,160
were getting, there were adverse event reports and Pfizer was required by, had a regulatory

1306
01:32:18,160 --> 01:32:23,160
requirement to gather data from a variety of sources, including VAERS, including UDRA

1307
01:32:23,160 --> 01:32:28,920
vigilance, including those kinds of things, including people calling up Pfizer and reporting

1308
01:32:28,920 --> 01:32:37,160
problems with the vaccine and taking, and then reporting to the FDA about what they

1309
01:32:37,160 --> 01:32:38,440
were finding.

1310
01:32:38,440 --> 01:32:44,200
And so the numbers of pregnant women were not in the trial.

1311
01:32:44,200 --> 01:32:50,780
Um, they were from this data set, which has all of the different biases and drawbacks

1312
01:32:50,780 --> 01:32:55,340
of all of the spontaneous reporting data systems.

1313
01:32:55,340 --> 01:33:01,480
And so you can't calculate a miscarriage rate because you can't calculate a denominator.

1314
01:33:01,480 --> 01:33:05,820
Even if you know how many doses were distributed, you don't know how many were taken.

1315
01:33:05,820 --> 01:33:07,980
You don't know how many were taken by pregnant women.

1316
01:33:07,980 --> 01:33:13,160
You don't know what week the women were pregnant, what week of pregnancy the women were in.

1317
01:33:13,160 --> 01:33:14,760
You don't know the under reporting rate.

1318
01:33:14,760 --> 01:33:18,000
And so you don't actually know the number of miscarriages that actually occurred, et

1319
01:33:18,000 --> 01:33:19,640
cetera, et cetera, et cetera.

1320
01:33:19,640 --> 01:33:26,520
And so it stands to reason that if a woman is pregnant and she takes a vaccine, she's,

1321
01:33:26,520 --> 01:33:31,240
it's more likely to be reported as an adverse event if she had a miscarriage.

1322
01:33:31,240 --> 01:33:38,160
Now there are many of some of the adverse event reports for pregnant women is just,

1323
01:33:38,160 --> 01:33:39,920
we gave this to a pregnant woman.

1324
01:33:39,920 --> 01:33:41,080
That's an adverse event.

1325
01:33:41,080 --> 01:33:43,400
Nothing bad has to happen in order for them to report that.

1326
01:33:43,400 --> 01:33:48,560
So, so they, you know, you, you get these high rates of like 82% of the pregnant women

1327
01:33:48,560 --> 01:33:52,360
in the trial had a miscarriage.

1328
01:33:52,360 --> 01:33:53,360
That's just wrong.

1329
01:33:53,360 --> 01:33:57,240
And so, you know, it's, it's like, it's so wrong at so many levels and you see it repeated

1330
01:33:57,240 --> 01:33:59,240
again and again and again.

1331
01:33:59,240 --> 01:34:03,520
And even though I believe in the, the, the, the book that they published, the report on

1332
01:34:03,520 --> 01:34:10,400
that, I believe they got it right in that final report, but it was repeated so often

1333
01:34:10,400 --> 01:34:14,520
prior to that wrongly that that's what's, that's what's stuck in people's head.

1334
01:34:14,520 --> 01:34:15,520
And I see that repeated.

1335
01:34:15,520 --> 01:34:18,160
It makes my blood boil.

1336
01:34:18,160 --> 01:34:24,640
You know, so daily cloud had, they, they have like a bunch of different reports that they

1337
01:34:24,640 --> 01:34:28,960
were releasing throughout their study of the trial.

1338
01:34:28,960 --> 01:34:32,080
And then they put a bunch of them together and published it.

1339
01:34:32,080 --> 01:34:33,080
You can get it on Amazon.

1340
01:34:33,080 --> 01:34:34,080
I forget what it's called.

1341
01:34:34,080 --> 01:34:35,080
You can go to the date.

1342
01:34:35,080 --> 01:34:39,360
If you go to the daily cloud website, I'm sure there's an advertisement for it.

1343
01:34:39,360 --> 01:34:40,360
Okay.

1344
01:34:40,360 --> 01:34:41,360
Thank you.

1345
01:34:41,360 --> 01:34:42,360
Yeah.

1346
01:34:42,360 --> 01:34:43,360
It's, it's this guy.

1347
01:34:43,360 --> 01:34:44,360
There you go.

1348
01:34:44,360 --> 01:34:45,360
That's it.

1349
01:34:45,360 --> 01:34:46,360
Yeah.

1350
01:34:46,360 --> 01:34:48,360
That's it.

1351
01:34:48,360 --> 01:34:49,360
Yeah.

1352
01:34:49,360 --> 01:34:50,360
Very interesting.

1353
01:34:50,360 --> 01:34:54,080
This is another interesting thing we could dive into, but what, what about you, Pierre?

1354
01:34:54,080 --> 01:34:57,440
Do you concur or is there something else that you've, you've identified for your,

1355
01:34:57,440 --> 01:35:03,400
I concur on the most regular error that I noticed around me is that people are systematically

1356
01:35:03,400 --> 01:35:09,600
telling me that the real world data has proven since the trial that the vaccine is safe or

1357
01:35:09,600 --> 01:35:12,400
effective and there is nothing more wrong.

1358
01:35:12,400 --> 01:35:19,240
Yeah, so far we haven't seen a single satisfying study with data, which allows us to conclude

1359
01:35:19,240 --> 01:35:26,000
that aside from trusting the FDA, and I believe we have covered why we should have questions.

1360
01:35:26,000 --> 01:35:27,000
Right.

1361
01:35:27,000 --> 01:35:28,600
Like who cares about the fires or results?

1362
01:35:28,600 --> 01:35:32,760
We know from, you know, from all these other studies after it was released that it's safe

1363
01:35:32,760 --> 01:35:33,760
and effective.

1364
01:35:33,760 --> 01:35:37,600
Well, I've got a dead friend who may beg to differ.

1365
01:35:37,600 --> 01:35:46,640
Well, on that note, going off on a high note, again, you guys have done tremendous work

1366
01:35:46,640 --> 01:35:47,920
and it's ongoing work.

1367
01:35:47,920 --> 01:35:52,400
I'm always very pleased when I get the new, from both of you guys, the new substack emails

1368
01:35:52,400 --> 01:35:59,440
into my inbox and you do make it, despite the inherent challenges in communicating this

1369
01:35:59,440 --> 01:36:02,200
information, you do make it easy to understand.

1370
01:36:02,200 --> 01:36:08,740
You do add context, you do add links and references to other things if people do need more context

1371
01:36:08,740 --> 01:36:10,760
or need a recap.

1372
01:36:10,760 --> 01:36:13,960
You link to each other's work, you link to other people's work.

1373
01:36:13,960 --> 01:36:15,440
So, so thank you.

1374
01:36:15,440 --> 01:36:16,440
Thank you for this.

1375
01:36:16,440 --> 01:36:17,880
Thank you for the ongoing effort.

1376
01:36:17,880 --> 01:36:22,480
And I highly encourage again, everybody to follow both, both substacks, which I'm going

1377
01:36:22,480 --> 01:36:24,400
to pull up one by one.

1378
01:36:24,400 --> 01:36:27,360
On our way out, Pierre, where can people find you?

1379
01:36:27,360 --> 01:36:29,000
What are your social media handles?

1380
01:36:29,000 --> 01:36:32,120
Where do you want to direct people to find more of your work?

1381
01:36:32,120 --> 01:36:41,720
On Twitter, I'm on my current sixth account on Canceled Mouse and on Substack, I'm OpenVAT.

1382
01:36:41,720 --> 01:36:44,240
Did you say your sixth Twitter account?

1383
01:36:44,240 --> 01:36:45,240
Yeah.

1384
01:36:45,240 --> 01:36:47,480
You've had to go through six.

1385
01:36:47,480 --> 01:36:48,480
Yeah.

1386
01:36:48,480 --> 01:36:51,800
I was banned for banned evaluations since I was anonymous.

1387
01:36:51,800 --> 01:36:53,800
You don't need to justify your ban, you know.

1388
01:36:53,800 --> 01:36:59,480
So publishing open data and code is dangerous nowadays.

1389
01:36:59,480 --> 01:37:06,160
Pierre is at Canceled Mouse on Twitter in case somebody, anybody didn't catch that.

1390
01:37:06,160 --> 01:37:08,760
Oh, that's you, Pierre.

1391
01:37:08,760 --> 01:37:09,760
Okay.

1392
01:37:09,760 --> 01:37:10,760
That's me.

1393
01:37:10,760 --> 01:37:16,480
We've been communicating recently and I was enjoying our communication, but I hadn't connected

1394
01:37:16,480 --> 01:37:18,360
the two of you yet.

1395
01:37:18,360 --> 01:37:19,960
It's not easy to keep track.

1396
01:37:19,960 --> 01:37:23,160
And now it's not about me anymore.

1397
01:37:23,160 --> 01:37:29,560
Pierre, you're also one of the only people who engage us on, I think, either Gab or Getter

1398
01:37:29,560 --> 01:37:34,560
is where you'll retweet or regab our stuff.

1399
01:37:34,560 --> 01:37:39,160
I stopped to use Getter because I was shadow banned constantly since I published an analysis

1400
01:37:39,160 --> 01:37:43,440
of US census data, which is deeply flawed.

1401
01:37:43,440 --> 01:37:45,440
Shadow banned from Getter?

1402
01:37:45,440 --> 01:37:46,440
Yeah.

1403
01:37:46,440 --> 01:37:47,440
Okay.

1404
01:37:47,440 --> 01:37:51,960
Well, this may have something to do with rounding the news episode that I'm doing this Friday.

1405
01:37:51,960 --> 01:37:55,880
So there's a fun little cliffhanger teaser for everybody.

1406
01:37:55,880 --> 01:37:56,880
Very interesting, Pierre.

1407
01:37:56,880 --> 01:37:57,880
I'm sorry to hear that.

1408
01:37:57,880 --> 01:37:58,880
No worries.

1409
01:37:58,880 --> 01:37:59,880
Okay.

1410
01:37:59,880 --> 01:38:00,880
Well.

1411
01:38:00,880 --> 01:38:01,880
Interesting detail.

1412
01:38:01,880 --> 01:38:06,800
You can see in code who the shadow ban is, if you're interested.

1413
01:38:06,800 --> 01:38:07,800
Okay.

1414
01:38:07,800 --> 01:38:10,200
Well, very interesting indeed.

1415
01:38:10,200 --> 01:38:12,400
Now how about you, Josh?

1416
01:38:12,400 --> 01:38:16,440
Where would you like to direct people to in addition to your fantastic substack?

1417
01:38:16,440 --> 01:38:18,400
Thank you.

1418
01:38:18,400 --> 01:38:22,760
I'm at JoshG99 on Twitter.

1419
01:38:22,760 --> 01:38:28,640
That would be the other way, retweeting lots of stuff.

1420
01:38:28,640 --> 01:38:29,640
Fantastic.

1421
01:38:29,640 --> 01:38:30,640
Okay.

1422
01:38:30,640 --> 01:38:32,760
Well, now I'll just do our plug here.

1423
01:38:32,760 --> 01:38:40,000
I highly recommend if you haven't been following us over here on locals, runningtheearthotlocals.com,

1424
01:38:40,000 --> 01:38:45,920
we've been having excellent community discussion in the locals chat as we always do.

1425
01:38:45,920 --> 01:38:50,880
And if you're not yet a member, highly recommend becoming so in order to keep track of everything

1426
01:38:50,880 --> 01:38:55,480
we're doing in this nice, easy, friendly, community-based format.

1427
01:38:55,480 --> 01:39:00,320
And if you want to support Running the Earth financially, you can do so for as little as

1428
01:39:00,320 --> 01:39:05,880
five bucks a month and you get access to super sweet, super exclusive weekly live streams

1429
01:39:05,880 --> 01:39:11,200
where we talk about stuff that we're not quite ready to put out into the main public sphere,

1430
01:39:11,200 --> 01:39:15,280
but within a smaller internal community brain trust, you might say.

1431
01:39:15,280 --> 01:39:17,040
We always have fantastic discussions.

1432
01:39:17,040 --> 01:39:19,680
So runningtheearthotlocals.com.

1433
01:39:19,680 --> 01:39:23,640
And yeah, that's our plug there.

1434
01:39:23,640 --> 01:39:28,880
Matthew, any final words before we kick ourselves out of this internet video stream?

1435
01:39:28,880 --> 01:39:29,880
Yeah.

1436
01:39:29,880 --> 01:39:30,880
I'm just going to say it again.

1437
01:39:30,880 --> 01:39:35,480
No notebook, no blueprint, no science.

1438
01:39:35,480 --> 01:39:41,040
As far as your trust at least in whether or not science has taken place, no notebook,

1439
01:39:41,040 --> 01:39:42,800
no blueprint, no science.

1440
01:39:42,800 --> 01:39:45,600
It's not communicated as far as we know it's a black box.

1441
01:39:45,600 --> 01:39:50,200
And that's not acceptable for the largest experiment in human history.

1442
01:39:50,200 --> 01:39:58,660
That may be acceptable for learning how to bake a new cupcake, but it's nothing on this

1443
01:39:58,660 --> 01:39:59,800
grand scale.

1444
01:39:59,800 --> 01:40:01,560
It's ridiculous.

1445
01:40:01,560 --> 01:40:07,400
And thanks to Josh and Pierre and all the people who have done detailed work to help

1446
01:40:07,400 --> 01:40:10,720
make that plain because you have to shine a lot.

1447
01:40:10,720 --> 01:40:14,440
It's so big that you have to shine the light in a lot of places to get to the point where

1448
01:40:14,440 --> 01:40:15,440
you can make that statement.

1449
01:40:15,440 --> 01:40:18,840
No notebook, no blueprint, no science.

1450
01:40:18,840 --> 01:40:23,000
Thank you for giving us the platform to share our work.

1451
01:40:23,000 --> 01:40:25,080
And you're welcome back absolutely anytime.

1452
01:40:25,080 --> 01:40:28,720
I know there's going to be further updates probably sooner than later.

1453
01:40:28,720 --> 01:40:31,800
And we had this one scheduled like a month ago.

1454
01:40:31,800 --> 01:40:37,040
So I'm sure we'll be able to get you back on even sooner than a month from now, if so

1455
01:40:37,040 --> 01:40:38,040
required.

1456
01:40:38,040 --> 01:40:42,760
So thank you again and thank you to everybody who tuned in today.

1457
01:40:42,760 --> 01:40:45,520
Yet another fantastic show.

1458
01:40:45,520 --> 01:40:48,760
And yeah, I look forward to seeing everybody.

1459
01:40:48,760 --> 01:40:52,900
Well tomorrow for our weekly locals exclusive and then I'll be back on Friday for as we've

1460
01:40:52,900 --> 01:40:57,600
teased here something a continuation of last week's show.

1461
01:40:57,600 --> 01:41:01,100
And it is interesting that this whole getter discussion has come up because it is directly

1462
01:41:01,100 --> 01:41:03,180
related to what I'm going to be talking about.

1463
01:41:03,180 --> 01:41:04,600
So there's your little cliffhanger.

1464
01:41:04,600 --> 01:41:32,760
We'll see you guys again very soon.