Ladies and gentlemen, welcome back to the rounding the earth podcast.
Rounding the earth is a multimedia education project based on the popular newsletter series
published on Substack written by applied statistician and educator Matthew Crawford.
Topics of discussion range from critical analysis of conventional wisdom to Bitcoin and everything
in between and in particular the ongoing plandemonium.
Our goal is a careful examination of important topics and perspectives shaping the world
that too few people talk about.
Subscribe to rounding the earth on local, Substack and Rumble to join a burgeoning research
community and to help us unflatten the earth.
My name is Liam Sturgis.
I'm a musician, music producer and writer slash editor coming at you live from the thankfully
sunny Vancouver, British Columbia, Canada, and I will be your host for today.
But as always, I do not do it alone.
Please allow me to introduce the author of rounding the earth and my cohost for this
and most podcasts, Matthew Crawford.
Good afternoon, Matthew.
And you are on mute, sir.
There we go.
Thank you.
That's me futzing with where I can watch the video in the chat as we go.
I'm still not quite used to that.
You know, all these, these whizzy techno gadgets that make a podcast happen, right?
And I've had a lot of good conversations lately, talked to a lot of amazing people.
But today's, today's guest, Josh, is one of the handful of people that I've counted on
the most.
Whereas, you know how sometimes you sift through information and you want to read things yourself.
You don't want to promote anything that might not be tight.
Well, Josh, Josh does tight work.
He's been, you know, one of those people, you know, every time I go through what he's
written, I understand it when I read it.
Right.
So it's one of those excellent qualities about somebody's capacity to handle information
and explain it well.
I'm really glad that he's here with us again.
I am as well.
And I'm very excited to let people know that we it's it's not just Josh.
In fact, we've got a second guest today.
We get a two on one.
Yes.
And I have been more and more aware of this gentleman's work, especially the ongoing
partnership of work with Josh.
And we're going to have the the duo of gentlemen sort of explain in more detail what they're
doing.
But I want to echo your excitement.
And let's use this opportunity to reintroduce our friend, Josh Gutskow.
Please tell me I got that right.
And Pierre of Open Vate.
How are you, gentlemen?
Hi.
Good evening, everyone.
Thanks for the lovely introduction.
Indeed.
Yes.
Pierre, I have to say real quick, I love that room that you're in, the architecture.
That cozy home.
It's an excellent retirement place from civilization indeed.
Well, it's yeah, it's absolutely beautiful.
Now, Pierre, can you confirm that I pronounced your handle correctly?
Open Vate?
Open Vate.
Yeah, but you did.
You did.
Okay.
Vaccine adverse event tracker.
I just learned that today.
And now so have the rest of us.
So let's do this.
Let's Josh, do you want to briefly reintroduce yourself to the audience who may not, some
of whom may be watching you or running the earth for the first time?
And then we'll have Pierre introduce himself to the audience as well.
Okay, sure.
My name is Josh Gutskow.
Let's go Gutskow.
So close.
Next time.
We'll do it in the green room beforehand.
Okay, next time.
My name is Josh.
I'm a professor in sociology and criminology at the Hebrew University.
I grew up in the US.
Prior to the COVID fiasco, I wasn't doing anything related to this sort of thing.
So I did do a postdoc at Harvard in health policy.
And at some point I started turning my attention to issues related to the problems with the
vaccines and that sort of thing.
And then when the clinical trial data started coming out from Pfizer, I realized that I
had the ability to transform the files from one format to another that people could use.
I was doing that and then I kind of slowly, slowly, I was busy with other things.
I didn't want to get into it.
But then slowly, slowly, I realized I'm one of the few people I know that has kind of
the ability to work with this type of data.
So I started working on it.
I was working on it kind of full time from like May for about six months.
And then I got caught up with other things.
And then recently, a few months ago, I connected with Pierre.
I had kind of seen him prior to that.
But I don't know what happened.
We got connected and then we started a collaboration.
And it's just been great because he's just extremely gifted and extremely creative and
extremely knowledgeable about this data.
It's actually been a delight to meet minds with somebody who is sort of into the data
and understands the nuances of it.
We can really talk about those things.
So that's where we're at.
And we've been kind of collaborating for a couple of months.
I have to say that I haven't had time to do much of a lot of the analysis that we're going
to be presenting.
Pierre has really been the backbone of this and doing a lot of the heavy lifting on the
data analysis.
And there's a good advantage to that because Pierre uses Perl and all of his code is open
source.
He puts it out so anybody can go and download the data and do work, rerun his code and get
the same results and see what he's doing.
It's very, very transparent.
Whereas the software that I have been using is proprietary, it costs money, and it's kind
of hard to share my work.
I'll stop there and let Pierre say more about himself.
Yes.
And Pierre, I just want to say I've been having a fantastic time following your updates on
your substack.
And I highly recommend everybody go and follow Pierre.
And of course, Josh's substack is included in the description.
We'll make sure to pull up any relevant articles as we go as well.
So long story short, everyone should go subscribe to both if you have not yet.
So Pierre, do you want to introduce yourself to us?
Yes, I will briefly.
Thanks to all for your kind words.
And my own background is a bit atypical.
I started by studying law.
I mainly perfected my IT skills during that time.
And I started in forensic and working with a big intelligence economic firm in Paris
and learned a lot on big data analytics at this time, then moved to professional gambling.
And I was mainly enjoying an interesting life when the COVID fiasco struck, to which my
life took an unexpected turn.
And I took some steps back as I wasn't able to appreciate the situation accurately.
I was detecting a lot of propaganda, but had no access to accurate information.
So I went in to work as an anonymous data, let's say, analyst, and put my skills to use
where I could find, let's say, work worth doing.
And I first started with adverse effects where at this time a lot of fake numbers were propagated
in Europe to decredibilize the proposition movement.
And from that moved, thanks to my friend Jeff Payne, to the Pfizer trial and was doing a
lot of attempts to reproduce the efficacy analysis on the fragments of data we had at
that time when I met Josh and shared the delight to meet someone who shared my perfectionism,
if not pushed it higher.
So where does your data come from?
Is it pan-European or are you focusing on French data?
On adverse effects, I worked on American and European data, the data vigilance and the
virus and a lot of countries' scales.
And on the Pfizer trial, it's mostly PHMPT, which is the data origin.
Yeah, the Pfizer data dumps that everyone's familiar with but don't know how to read.
Okay, very good.
Well, just organizing is half the battle, right?
Well, and I'll say I was surprised to see there are still dumps coming.
You know, they've sort of exited the news cycle, but they came up last night in a meeting
I was in and I went to check and indeed as of earlier this month, they're still dropping
stuff.
What is being disclosed at this point and is it relevant to the research you guys are
doing?
Oh yeah, absolutely.
The stuff that was dumped this month was not really...
We've been working a lot on the sort of subject level data, you know, and then they have kind
of reports.
So Pfizer basically gave the FDA the data and a bunch of reports about the data.
And so we've been mainly trying to use the data itself, but this month what was released
were a bunch of different reports.
And one of the things that I was going to get to later, but I can bring it up now that
Pierre had recently worked out was that...
So you know, in mid-December they started giving people that were in the placebo group
a real doses of the vaccine.
Mid-December 2020.
Mid-December 2020, yeah.
Thanks for that clarification.
So if you look at the number of adverse events that they reported, given the timeframe that
they had, it's a far higher rate of adverse events than the original treatment group.
Interesting.
And he had found that out and then in these PDFs that were released, there was a memo
from them that basically says that.
And one of the weird things about it is they say as expected.
They were expecting that.
And so we're trying to figure out why in the heck were they expecting that.
We don't know.
One hypothesis is that they gave some priority to older people in giving them the real vaccine.
And so they may have been expecting a higher rate of reporting of adverse events because
these people are older and more frail and sicker.
Because remember, adverse event doesn't necessarily mean it's caused by the vaccine.
It's just like, sure, well, these people are more prone to morbidity in general and therefore
we'd expect a higher rate.
We don't know.
Were the participants not randomized?
Have they stated their method for sifting cohorts?
In terms of the...
In a randomized control trial, you would have some sort of randomization method to determine
in person to push them into a cohort.
So you make it sound like one hypothesis is that the cohorts were not of similar health
status which would already indict the trials.
No.
Okay.
No.
What I mean is that when they...
And I'll show you this.
I have a graph that shows this.
We should dive into this because we've got a lot of ground to cover.
But basically when they went to give the placebo group, people in the placebo group, when they
get to give them the real dose, they prioritized elderly people in the crossover group.
But otherwise they're balanced.
The placebo group and the treatment group were well balanced.
Okay.
Just throwing out one quick hypothesis is that shedding does matter.
And that if they expected shedding to have affected the placebo group, then shedding
may have primed them for a more harsh reaction later on.
Okay.
But why?
I mean, they...
I don't know.
I mean, maybe, but it doesn't seem like the sort of thing that they would admit, they
would say the quiet part out loud and say as expected.
Yeah.
It's still weird no matter how you slice it.
And I'll tell you something.
Okay.
There's another...
There's something that's recently come to light is that the vaccine trial was...
The doses that were given to almost everybody in the trial were manufactured with a particular
process, they call it process one, which was for these small batches.
And they found they couldn't really scale it up, so they created a new manufacturing
process.
They call it process two, which they tested on 250 people in the entire trial.
But by the time the placebo crossovers were ready to go, they already had...
Process two doses, that's what they were sending out.
That's what everybody in the world got.
And I believe that that was also what the crossover placebo group got.
And if you look, we have documents from the EMA and other things, there's a much lower
purity of the process two batches.
And so that may be why they were getting a higher rate of adverse events.
We don't know.
Okay.
Well, because we've got a lot of ground to cover, as you say, do you want to dive into
this presentation we got here?
Comernati or Comernati.
This is our sort of investigation.
And here's a brief little...
Oh, you know what?
I got to go to this...
Okay.
So the overview is we're going to look at how long the trial actually lasted.
This is something that people talk about, but we can look at with hard numbers.
We want to... we're going to talk about this issue of missing subject or missing subject
IDs that we found.
We're going to look at the issue of trial blinding and unbalanced protocol deviations.
Then we're going to look at imbalances.
When I say imbalances, it means that the placebo group and the treatment group were not...
They should be being treated and doing everything the same, but they're not.
There's some imbalance between them.
We're going to look at imbalances in testing rates, PCR testing rates.
And then we're going to look at sort of this distinction between PCR positives and COVID
cases.
So how long did the trial last?
Oh, so background here, you guys, we've already done this, so this is sort of irrelevant,
but this is where we're getting our data.
This is the documents that were released via a Freedom of Information Act request through
this Act, the legal action of the public health and medical professionals for transparency.
There's a lot of documents out there and there's a great little app at the URL there on the
bottom right that was developed by some very talented people that you can go through and
you can do word searches through all the PDF files and things like that.
They've got a lot of other nice things at that website.
So basically, beginning on December 14th, three days after the EUA was granted, they
began unblinding the trial participants, the trial subjects.
And you can kind of get a sense of the rate at which this happened.
Now it's important to mention that the data that we have goes until March 13th.
That's the cutoff, okay?
And the application for the full approval of Comornati is based on data that goes through
March 13th.
And that's why that's the data cutoff.
That's what they submitted to the FDA for this approval.
So you can see that they're unblinding the trial subjects, placebo and treatment groups,
fairly evenly throughout that period from December 14th to March 8th or March 13th.
And then you can look and say, okay, well, from the time that people received their first
dose until they were unblinded.
So once you're unblinded, this kind of ends the trial for you anyway, because knowing
what type of...
If you've got the placebo or the treatment, it can affect the way you behave.
And that's the whole reason that we try to do double blinding is because we don't want
to affect the way that people will act or behave or create some kind of placebo effect
or whatever.
So basically, you get 137 days on average.
The average number of days that anybody was in the trial from the first dose to unblinding
was 137 days, so a little over four months, four and a half months.
And that tracks them until the day they were actually unblinded.
But if you look at how the unblinding went by age group, what you can see is that they
weren't unblinding by age group equally.
At the very beginning, there's a bit of a preference for 16 to 55s, but then they started
unblinding the older age group at a higher rate.
And so basically what happens, what this essentially means is that the trial really ended on December
14th.
Because once you create an imbalance in the participants who are in your study, in this
case, they're creating an age imbalance.
They're saying, okay, we're going to go and give these people the...
We're going to unblind this group of people, this subgroup of people.
If they had started unblinding randomly, okay, so then we could say that the trial really
goes until people are un...
Each individual is unblinded.
So if you look at when people started, got the first dose until December 14th, the average
number of days drops from 137 to 97.
So you can basically say that the trial lasted basically three months, okay?
Just a little over three months.
And then that's it, right?
And then the treatment arms and the ages get unbalanced and it's over.
They also unblinded right at a time when you would introduce a new form of bias called
risk adjusted person days, right?
When you go into that period of high infectivity, and we saw this in one of the Israeli trials,
I can't recall which one, maybe the BARDA trial.
In one of the two retrospectives, they didn't adjust for the fact that there was a significantly
larger number of people unvaccinated during the peak of COVID-19.
And so you have much more risk per day in the unvaccinated group and that has to be
adjusted for.
And it looks like when they open up that unblinding process, I mean, that's the beginning of COVID
season.
Right, right.
Yeah, so there's a lot of room there for different types of bias to sneak in.
So now I'm going to get into the...
That was something that I had worked on.
Now I'm going to get into some of the stuff that Pierre and I have been working on together.
And this is a synopsis of some of the major posts on his blog that I'm going to be distilling
down into the key points here.
So one of the things that has puzzled us and been very troubling is what I call the curious
case of missing subject IDs.
So basically when a subject is...
They have a screening process for the trial.
The company that was hired to basically organize the trial is called ICON.
And when people wanted to sign up for the trial, they would first contact ICON and they
would have a screening, a pre-screening done through ICON.
And then if they pass that pre-screening, then they would go into the actual site for
another screening.
And when they went into that site, essentially they signed the...
Once they signed the informed consent, which was at the very beginning or supposed to be
the very beginning, they would be assigned a subject ID number.
And if they didn't pass the screening, that second stage of screening, if they didn't
pass it, they would still have a subject ID.
They were still in the data.
It was just a screen failure.
They were not randomized to placebo or treatment group.
They're considered screen failure.
And so the data that they've released includes all of this group of people, those who are
randomized placebo treatment and those who were screened but failed the screening.
And when a number, a subject ID number is assigned to somebody, it's done consecutively.
So at a given trial site, they start with the number 1001, and then the next person
that comes in is 1002, and 1003, and 1004.
The only way you wouldn't get assigned a subject ID number is if you didn't sign the informed
consent.
So they're basically assigning numbers essentially the moment you walk in the door.
So they're doing it consecutively.
But if you look in the data, you see that there are a lot of numbers that are skipped,
a lot of subject ID numbers that are skipped.
So you see for a given site, you'll see 1001, 1002, 1003, and then suddenly you see 1005.
1006, and you're like, wait, what happened to 1004?
And generally, this form of indexing, it's meant not to have gaps in the end for the
most part.
Exactly.
And then you might find a reason to have deleted a record, do this or that.
But if your procedures are good, you've created a process from beginning to end such that
you do not have gaps and that if there's any data exclusion, you know why.
The record is still there.
You know why the data is being excluded.
You've tracked everything.
Exactly.
Now we do have, we have from Brooke Jackson, email or some kind of screenshot of a form
that allowed the trial site to request the deletion of a subject ID.
So we know that this could happen.
But you're correct that it's not something that should happen very often.
So we can accept that it may have occurred occasionally, but we have several anomalies
that make us suspicious that this wasn't just computer or user error in every case.
One of those anomalies is that we see that in many cases, there were several consecutive
ID numbers that are skipped.
So you have two numbers skipped, three numbers, all the way up to nine consecutive numbers
that are skipped.
So it's hard to conceive of how that could have happened by computer error or subject
error or human error.
So that's one of the things about it.
The other thing is that we see that, you know, we would also expect it to be kind of like
randomly distributed across the sites, right?
That people would be making mistakes kind of randomly.
But we don't see that.
We see them concentrated in a handful of sites and especially see it heavily concentrated
at the Argentina site, Buenos Aires, right, where they have, you know, 12 percent of the
trial subjects, but 37 percent of the skipped ID numbers.
Now what is the concern here?
What is the suspicion?
The suspicion is that at some point they went back and deleted subjects whose results or
something was inconvenient, was something that they wanted to try to hide.
Maybe they died.
Maybe they had a serious adverse event.
We don't know.
And again, it's a suspicion.
We haven't been able to verify, you know, whether this could happen by some fluke of
the software.
We've contacted the FDA.
We've contacted the ICON.
And so far we haven't received an answer.
If I could make two numerical observations there.
OK, this looks like, correct me if I'm wrong, 301 missing subject IDs.
Yes, 301 missing subject IDs.
Which is in the ballpark of the entire effect size in terms of the total number of people
between the two arms who got COVID in the arms.
So that's one point that I want to make.
The second one is 44,000 participants and there was a serious adverse event ratio of
0.7 percent, if I recall correctly, and 0.7 percent of 44,000 is 308, which is suspiciously
close to 301.
Just throwing those numbers out there.
OK, great.
Well, here's some more suspicion to add to the file.
OK, so we're looking at Argentina now.
There's been a lot made of the Argentina site.
But one of the things is that, OK, so the largest number, 111 of these 301 missed trial
subjects were from the Argentina site.
And on this day, when they had nine consecutive missing ID numbers, they had an additional
eight.
So they had 17 on one given day.
This is August 21st, 2020.
They had 17 missing subject ID numbers.
I'm calling them missing because they should be there.
They went missing.
We don't know why.
OK.
Now here's something that's an interest.
Now remember, we don't know when they were deleted from the data.
We see that we see a snapshot of the data that was created on August.
I'm sorry.
On March 13th, 2021, these people could have been deleted on the first day, you know, August
21st or any time in between.
We don't know.
All we see is this one snapshot.
Now guess who was recruited to the trial on August 21st?
Augusto Rue.
OK.
Now for people who aren't familiar with Augusto Rue's story, Augusto Rue is an Argentinian
lawyer who was volunteered for the trial and became very ill just after his second dose
and ended up in the hospital.
When he went to the hospital, they took a PCR test.
It was negative.
He basically had pericarditis.
When he reported it to the trial and the doctors at the hospital said it was related to the
vaccine, and the nurses there said they've been seeing hundreds of people coming in from
the trial with adverse events.
You remember there were like over 5,000 subjects from Argentina in a very short space of time,
4,000 in that space of time that were being vaccinated.
So OK, sorry, I'm getting off track.
So he had pericarditis.
He calls the trial site to tell them what they have and they basically write it down
as he has pneumonia.
And then a few weeks later, the trial site in Argentina is contacted by the trial sponsor,
BioNTech, asking them to change his adverse event from pneumonia to suspected COVID-19.
And even though he had a PCR test that came out negative, they put it down as suspected
COVID-19.
Now here's an interesting loophole in the protocol is that they were not supposed to
count cases of suspected COVID-19 as an adverse event.
So if your adverse event was designated as a potential COVID-19 case, they didn't have
to count it as an adverse event.
Now in Augusto Rue's case, it is in the count of adverse events, but we don't know if other
cases of suspected COVID-19 show a similar, you know, have if some of them were just a
way to kind of cover up adverse events.
And by the way, Pierre has tried every which possible way to recreate the numbers of suspected
COVID-19 cases.
You remember Peter Dochey wrote about this showing that if you look at suspected COVID-19
cases, the efficacy is like 19% or something like that.
He's done a heroic job of trying to replicate those numbers.
He's come close, but not quite.
So we still don't know how those numbers were exactly calculated.
Now here's another thing, okay, another weird thing about missing subjects or related to
the missing subjects problem.
There is a file.
So far we've only gotten the version that was submitted to the EUA, but I believe a
similar version will be coming through the FDA pretty soon.
This is a list of investigators and number of sites.
Now the total number of subjects screened they have there, this is by the way includes
the adolescent trial, 48,092.
In the subject level data, the official data set for analyzing the efficacy that just came
in March, by the way, they have 48,091.
So they're off by one.
But if you look on the lower right-hand side, it says subjects screened per site and subjects
randomized or entered per site.
So you see, now this is the page showing the Argentina site, 5,896 screened, 5,615 randomized.
Now in their data, in the subject level data that we have, we know who was a screen failure
and who was randomized, right?
So we know for each site, we know the total number of people they screened, how many were
randomized, right?
How many weren't.
The numbers don't line up.
They line up for some of the sites, but for many of the sites, there are basically, if
you go by the numbers in this document, there are far more people who were screened but
not randomized according to this than in the actual data set that we have.
So this is another huge anomaly between the number of, just basic numbers of the people
that should be in the study just don't match up.
By the way, feel free to stop me if you guys have any questions or something you want to
add.
Maybe just a very brief thought.
One of my worries with the trials is that no one ever tested to see, we have a proxy
test for disease, which is this PCR test.
And the PCR test that was used, it's not even in use anymore, but one of the things that
did not take place that ordinarily would have taken place during a longer trial procedure
is a test to make sure that vaccination did not confound that proxy test.
Okay, right, right.
Okay, wait, there's going to be a perfect place later on to get into that discussion.
Okay, okay.
I do want to finish this sentence real quick.
It is possible that that confounding depends on some factor that they could screen for.
Yes.
Well, and that assumes that they even would need to screen, right?
They'd have to get around the blind in some circuitous fashion, right?
That's what I understand you to be meaning.
Well, I just mean maybe you can identify a portion of the population for whom the vaccine
does or does not confound the PCR test.
Oh, I see what you're saying.
Okay.
And scope the results that way.
Right, yeah.
One thing that, I mean, so one of the things I always like to point out here with this
trial is that it's not a double blind trial.
It was never billed as or presented as a double blind trial, okay?
It was called what they call a observer blinded study.
And if you go through the protocols in the different documents, you see kind of like
this division of labor between who was supposed to be blinded and who was supposed to be blinded
at the site level and at the study level.
So you have things like, okay, you're going to have a blinded study coordinator, but the
study manager is going to be unblinded.
Like what is the division of labor there, right?
And we know from, you know, from Brooke Jackson, the sites she worked at, there was just no
blinding going on whatsoever, right?
I mean, terrible protocols.
We don't have any reason to suspect that the other sites were any better, right?
But, and also there's just this, you know, how can you have a handful of people working
side by side and some of them are blinded, some of them are unblinded.
Now one of the, excuse me, one of the major justifications for this at the site level
was that the vaccine requires a very special preparation, right?
It's both storage and preparation.
So there was somebody there and that presumably anybody doing the injections at least would
be able to tell the difference between the study, you know, the investigational product
versus the placebo.
Okay.
I don't know that that justifies the level of unblinding that went on at the site level,
but that is their justification for that.
Okay.
And there is some anecdotal evidence that, you know, the subjects knew what they were
getting, right?
Isn't that, it's not rocket science.
Okay.
Now at the study level, there was also a lot of unblinding.
One of the justifications for that was that they needed to do statistical analysis while
the trial was ongoing.
Okay.
Because they were trying to do this at the speed of science.
And another interesting justification, if you look at on the right-hand side study level,
the medical monitor for adverse events.
So they had an unblinded person that was monitoring the adverse events.
Justification for that was they wanted to look for ADE, right?
Antibody Dependent Enhancement where the vaccine would make the disease worse.
So they were kind of on the lookout for this.
They were worried about this.
And I guess they thought if they started to see it, they would stop the study or something
like that.
But what this also does is it gives them free rein to go through the adverse events, call
up the site and say, hey, can you reclassify that adverse event as suspected COVID-19?
That sounds like something they did in Argentina.
And it was something they did in Argentina.
And we also know from Brooke Jackson that they asked the site that she worked at to do the
same thing.
So this was something that was definitely going on.
And again, we don't know how many sort of adverse events were swept under the rug with
this type of methodology.
But it's very suspicious.
Josh, just to interject, you just alluded to the notion that the recipients, the participants
in the study, people having their arm injected with something may have known as a matter
of fact kind of across the board whether they were getting the shot or the placebo.
Can you just elaborate?
Is that what you're saying?
And if so, that has some interesting implications.
Right.
Well, definitely the person administering it knew.
To what extent the people...
So I've talked with Augusto Roo, he said a lot of people knew that they didn't get the...
Felt that they got the placebo or they didn't get the placebo.
And we're going to see a little bit of evidence of that too.
I'm not saying everybody, every last person in the study knew that.
But I think there's good reason to think that many people were aware of it.
And part of why I pointed out is there was a video that it may have been JJ Cooey was
going over a number of weeks or months ago where there was somebody right at the beginning,
maybe it was December 2020, a high profile academic who was discussing his...
He was promoting, get out the shot campaign.
And he said that he was a part of one of the trials and he was not aware at that point
if he had gotten the placebo or the actual product.
And so this is why I bring this up is as we're starting to look back in other contexts and
try to examine what people knew when and who had authority that was abused or not, so on
so forth, it is interesting to add a layer of...
Is there a layer of maybe that wasn't a totally truthful statement when he said one way or
the other.
Just one example.
Right.
And I mean, there's a difference.
There's also, there could be shadings of knowing and thinking, right?
I don't think they gave me the real thing.
I think they gave me the placebo.
Because I feel completely fine.
So it can't have been the real thing.
But now let's look at some evidence.
The process that causes people to think through that is bad.
Right.
And also, again, the people conducting the study, many of them were aware of who was
getting the placebo and who wasn't, which it just goes against the best practices that
we have.
It's hard to imagine that they couldn't have done a better job.
First of all, the person preparing the dose didn't have to be the person administering
it.
So the person administering it didn't necessarily have to know that they may have been able
to do a better job of making the placebo more like the product.
I don't know.
But let's look at some evidence suggesting that the blinding...
This is what I call the Swiss cheese method of blinding.
Okay.
It's not double blind.
It's not observer blinding.
It's Swiss cheese.
And let's look...
So one of the ways we can look at that is we can look at imbalances across the treatment
and placebo groups in certain protocol deviations.
So whenever you deviate from protocol, a record is supposed to be made of it.
And so we see some odds.
Here's a handful of the protocol deviations where there's an imbalance across the arms.
Each of these individually is statistically significant.
I haven't done a test to look at...
I forget what they call it.
They're repeated tests.
Do you know what that's called, Matthew?
When you do a bunch of significance tests, you have to take into the fact that you've
done a bunch and some of them didn't come out significant.
Right.
We could flip coins all day and eventually we'll get a statistically significant at
the end of the day, a group of tail result.
So typically we correct by acting as if they were all part of the same pool.
Yeah.
So we have to go through and nail this down a little bit better.
But just take this at face value for the moment.
So we see, for example, an imbalance in the potential COVID illness visit not done when
required.
Meaning...
What does this mean?
It means that when people felt... when people had a symptom, when they weren't feeling ill,
they were supposed to call in to the site and either do a visit or have some kind of
phone call with the doctor about it.
So what we're seeing here is that the people in the treatment group were less likely to
do a visit when they were supposed to do a visit.
And we don't know why that is.
It may be that they knew that they had gotten the vaccine.
And so they're like, well, I don't have COVID because I got the vaccine.
I don't need to go in for a visit.
That might be.
I don't know.
Right.
We don't know.
We have...
Now here's something.
The next one is something that actually works kind of in the favor of the placebo group,
which is that they were collecting swabs from...
I'm sorry.
No, this doesn't work in favor of...
Most swab collected at a visit where it's not required.
So they were looking for infection from COVID at a higher rate in the placebo group than
in the treatment group when it wasn't required.
And then the next one is where it favors the placebo group.
They're not collecting swabs at a higher rate in the placebo where it is required, et cetera,
et cetera.
Now here's, if you go down, look at this one.
This is receipt of flu vaccine.
No, I'm sorry.
Receipt of other coronavirus vaccine.
You can see that there were a lot more people in the placebo group who were out there trying
to get another coronavirus vaccine.
Now this was like in December, January when it had come out probably or something like
that, or maybe there was another trial going on for another vaccine in the area.
And they're like, well, I didn't get lucky with that one, so I'm going to go out and
try another.
But by that huge imbalance, you can see that there were a lot of people in the placebo
group who felt strongly enough that they had gotten the placebo.
They went out and got another vaccine.
And then at the very bottom, you see urine pregnancy tests not performed.
So it was more likely to happen in the placebo group.
Right?
Well, why would you not do a urine pregnancy test?
Well, she got the placebo.
Why do we need to do a pregnancy test?
Right?
Again, we don't know exactly what's behind these imbalances, but it's an indication
that the blinding was far from watertight.
Okay?
Now, of course, one of the biggest imbalances is one that Matthew is one of the first, if
not the first to point out.
Now, this is from a table where they were looking at the efficacy.
They're doing efficacy calculations, and they're taking people out of the efficacy
calculation.
They're excluding.
These were people who were excluded from the efficacy calculations.
It doesn't mean they were excluded from the trial.
They weren't excluded from the trial.
They just weren't included in this calculation.
Okay?
So, it's a huge imbalance where you have that left column of numbers is the treatment group,
the middle column is the placebo group, and you see this big imbalance where you have
a lot more people excluded.
Okay?
Now, this looks bad, but I got to tell you, I don't think there's anything to it.
Okay?
And I'll tell you why.
First of all, we know who got a PCR test, who complained of symptoms, who got PCR tested,
and what the PCR test results were.
And there's no imbalance here between...if we look at this group of people who were excluded
on these other important protocol deviations, we now know who those were.
There's no imbalance in the number of people who got positive PCR test results.
One interesting thing about all of these people, both in the placebo treatment group, is they
all at some point reported symptoms, like a COVID symptom.
All these people, this 371 or 372 number, you're saying that all those people reported
COVID symptoms?
One of the nine official COVID symptoms, yes.
Okay.
So I want to throw this one back into the mix because you say that it doesn't look like
it might affect the efficacy calculations, but if the vaccination confounds the PCR test
and you're saying that all of these people showed symptoms, then I would throw that back
into yes.
It absolutely does matter that I, as a statistician, could not sign off on an efficacy calculation
with that kind of lopsided exclusion.
Right.
Now, what are these important protocol deviations?
So we can kind of whittle down or look at the reasons that were given for these other
important protocol deviations.
And so when I have here 105 versus four, that's 105 people in the treatment group versus four
in the placebo group.
So there's a dosing or administration error.
By the way, 52 of those 105 come from the Argentina site on October, I'm sorry, on August
23rd, 2020, 52 people at the Argentina site were given a higher dose of the vaccine than
they were supposed to.
I think it was 56 or 62 milligrams instead of 30.
And so they were told about this and they were, so they were told about this and then
they were given a choice if they wanted to continue or not continue.
Now okay, I didn't make this, I forgot to make this point earlier, so I'm going to come
back to it now.
That was August 23rd.
Now remember August 21st, two days prior to that, is when Augusto Rue received his first
dose and it's also the day when those 17, with those 17 missing subjects.
Okay.
So those missing subjects got their first dose on the same day as Augusto, which means
they were supposed to come back three weeks later and get their second dose on the same
day that Augusto got his second dose.
Now what if Augusto had an adverse reaction, a severe adverse reaction because there was
something wrong with the dose that he got on that second day?
That means that all of those other people that came back on the second day or many of
them could have also gotten something wrong with their dose, right?
We saw it, we see here that there were 52 people on one day where they just totally
screwed up their dosing.
Maybe they did it or something else happened on that other day and they caused some severe
adverse reactions in people to such an extent that they felt the need to erase those subjects
from the trial.
Okay.
That's a suspicion.
We don't know if that's what happened, but it's just weird that there are so many people
missing a day where we know somebody had a very serious adverse reaction from the vaccine.
In the old days, people did science with a notebook and that notebook never disappeared.
That was the gospel of that experiment.
Right.
And here, we have essential... Pierre has been saying we need to find a way to do an
audit of the actual database because all we see are extracts from the database.
There are many different ways to manipulate the extracts from the database, which is basically
all we have.
Okay.
So you see the IP, the investigational product was administered that was not deemed suitable
for use by Allmac.
Allmac was the company that was in charge of logistics, storage, and shipping of the
vaccines.
So if they had a shipment of vaccines and they saw that the temperature rose above a
certain amount, then those doses were no longer deemed suitable.
For example, that could be one reason why you get this.
We have incorrect vaccine allocation assigned to the subject.
And then the last one is this IP administered prior to Allmac authorizing it for use.
We don't know what that means.
We don't know why the vaccine wouldn't be... At what point is Allmac authorizing it?
Why would they give it prior to that?
We don't really know what that means exactly.
Pierre thought maybe it was an indication about the process two versus process one.
We don't know.
One other just sort of interesting thing about that protocol deviation is that it's actually
not coded in their data as an important protocol deviation.
Now, these people were taken out of the efficacy analysis on the basis that they had some quote
unquote other important protocol deviation.
This was the only protocol deviation these people had.
So they consider... Apparently, everybody who had this protocol deviation was taken
out of the efficacy.
So it could just be a coding error where they forgot to code this as important.
I'm not really sure.
But when you take all of these together, you basically end up with data that's no longer
imbalanced.
Now you could say, well, maybe there was some grand manipulation going on here where they
were giving these people these protocol deviations to take them out and they were manipulating
the results, the efficacy results on that basis.
I don't think there's anything to this in a sense.
This is not our smoking gun.
This is not... That's my opinion.
Okay.
So now we're going to get to another issue which is related to the testing that they
did.
Okay.
Josh, I'm sorry to cut you off before you move on to the next thought.
Point of clarification, you might've said this, but Allmac, it's a drug discovery company
that was running one of the trial sites.
It's equivalent to Ventavia Research Group, right?
No, no.
They were running the logistics of shipping and storing the doses, the investigational
product.
Where in the hierarchy of the large tree of organizations involved in the project would
they be compared to, for example, Ventavia?
They would be above Ventavia.
It would be like maybe contracted by ICON to run the logistics of the storage and shipping
of the vaccine.
Ventavia is a node.
They're the network for distribution.
Yeah, yeah, yeah.
Exactly.
That is helpful.
Thank you.
That's my understanding.
I could be wrong.
Okay.
So now we're going to move on to the issue of testing.
So the protocol says if you have one of these nine symptoms and a positive PCR test within
say four days of symptom onset or resolution, then you would be counted as a symptomatic
COVID case.
And that's what they studied.
That was their primary efficacy endpoint.
They were only looking at these symptomatic COVID cases.
Now this trial subjects were instructed to call in and report a wider range of symptoms
because they also had the secondary list of COVID-19 related symptoms and stuff like that.
But that's basically, this is how the testing work.
So what they should have done really, if they wanted to do this very carefully, is they
should have tested everybody at regular intervals, done a PCR regardless of symptoms.
They didn't do that.
You had to call in and tell them you were having a symptom and then they would instruct
you either to come in and have a PCR test done or people were also given swabs to do
at home.
And all of the testing for the trial of whether or not you were PCR positive was done at one
central laboratory, a Pfizer laboratory in New York.
Okay.
And this here's a little globe picture posted by Jickey Leakes showing that sort of distance
from how absurd it is to think that you would need to send a swab all the way from Argentina
to New York City for this rather than just have it tested on some local machine.
But the fact that all of the testing that would allow for an efficacy analysis was being
done at a central laboratory controlled by Pfizer raises one's eyebrows that they had
levers there that they could manipulate potentially to make the trial outcome come out as they
pleased.
And for anybody listening, make no mistake, there are hundreds of PCR labs in Argentina.
Right now, if you had a central PCR, if you had one of these central tests done, the results
for weeks.
So if you actually wanted to know if you had COVID, you would have to do a local test.
In some of the trial sites, the trial site itself was offering to do a local test with
a local machine that would be tested, run through a local machine, and you would get
a result very quickly.
In some cases, you had to go to your doctor to do that, or you had to go to a lab.
You couldn't rely on the trial site to do that.
So we have this distinction between the central tests controlled by Pfizer and the local tests.
Okay.
Now, I'm not going to get...
There's a lot of nuances here, because in the protocol, they had three different PCR
testing assays approved.
They said, if in order for a PCR result to count as a COVID case, it has to be one of
these three assays.
Anything else won't do.
And the central assay, they used one test as a CIFIID PCR test.
Now, many of the local tests weren't done on any of those three assays, but that doesn't
mean that their test results are invalid.
So here's our distinction, central and local.
So here's where we start to see a lot of shenanigans.
So one of the things that we see...
So one of the things that we see is that the treatment subjects were less likely to have
local tests done.
That's throughout the entire trial.
Well, we don't know why that is, but it's a strong indication of a lack of blinding,
right?
Well, first of all, it could be that the trial site operators discourage people who...
Disguise subjects from getting a local test done because they don't want a mismatch of
results between what the local test is saying and what the central test is saying.
Maybe they're trying to hide it.
It could be simply the treatment subjects saying, well, I got the vaccine.
I obviously don't have COVID, even though I have a symptom, but I'm not going to bother
getting a local test because, hey, I'm vaccinated, right?
I mean, it could be something like that.
We don't know what this variation is.
What's interesting though is that when we look at the data cutoff for the emergency
youth authorization, the data cutoff for that was November 14th, okay?
So they only use data up until November 14th, but if we look at the testing rates before
and after that date, we see a big change.
And we don't know what explains it.
We see a big increase.
So both placebo and treatment group subjects are sort of more likely to have a local test
done after the EUA cutoff.
That might be simply that there was a greater availability of tests at the local level at
that time.
We don't know.
I don't think that's what it is because there was still plenty of tests to go around before
that, but you never know.
Now let's look at the central testing rates.
If you look overall at the central testing rates, there's no difference between the rate
at which the treatment group and the placebo groups were tested.
But if you look at this EUA cutoff, you see this is very interesting where prior to the
EUA data cutoff, the treatment group was less likely to get a central test.
So I call in, I have a symptom, they're supposed to do a central test.
Low and behold, we see that they weren't doing that equally for the treatment group and the
placebo group.
But of course, if no central test is done, then you're basically never going to get a
positive PCR result.
Now that difference is small, 84% versus 87%, but it is statistically significant.
Moving forward, okay, so it isn't just the testing rate that this thing is going on.
We can look at the results that they were getting from local tests that were done and
compare those to the central tests that were done.
Now remember that there were also a lot of other PCR tests that were being done according
to protocol.
They were supposed to get a PCR test done on the first day when they got their first
dose.
They were also supposed to have a PCR test done, regardless of symptoms, when they came
in for their second dose.
And even a month later when they came back, they were supposed to get a blood test done
to test for their antibody levels that were often given a PCR test at that point.
But if you compare-
But tested before or after they took the dose?
I don't know.
But basically what I'm showing you here are- okay, these are positive.
If we take all of the PCR test results, local tests and central tests, put them together,
what we see is an imbalance across the placebo group and the treatment group.
This is a very busy table, so I'm just going to kind of give you a sense of- just go down
to the very bottom row, this is symptom-driven tests.
These are the protocol, per protocol tests.
We can look at what percentage of people who had a positive PCR result, either central
or local, that were counted as a COVID case for the efficacy analysis.
In the placebo group, 79% of people with a positive PCR test result, symptom-driven positive
PCR test result, were counted as cases compared to only 52% of the treatment.
There's this huge imbalance across placebo and treatment group, right?
Why is it that these people in the treatment group who have symptoms, they have a positive
PCR test result, but they're not being counted as a COVID case?
What gives?
And the answer to that is that essentially the people in the treatment group who had
a positive local test were more likely to have either a negative central test or a missing
central test compared to the placebo group.
A missing central test?
Or a missing central test, yes.
Well that's interesting.
We don't know what that means.
It could mean that they did it and it went missing, or it could just mean that they never
did it, right?
We don't know what stage of the process it was missing.
It could be that it was never done.
I just want to point out for the audience, that alone, if there's not a good reason for
that bias, that would engineer 34% efficacy already.
So if these factors add up amongst each other, that's basically a three to two ratio.
Right.
And now I want to say something about these numbers quickly.
These are numbers I calculated.
Pierre has calculated the same thing.
He didn't get exactly the same numbers here, and we haven't had a chance to kind of come
together yet and work out an agreement on exactly what we're doing.
But his findings were similar, right?
So we know this was going on, these numbers might not be exact, but this is a major thing.
And this I think comes back to your point, Matthew, about this idea of the test not being
sensitive.
So why is it that when the treatment group has a positive local test result, the central
test is more likely to come back negative?
Is that because of something related to the test, the central test not being sensitive
to people who are vaccinated?
Or were they pulling some other lever, like not doing the test as high a cycle count,
as the placebo group?
Right.
Or maybe they were just fabricating it.
We don't know why there was this.
One way or another, I just want to point this out for people who have been following the
story of how good are the PCR tests, right?
Even if you want to call it a test, it's a proxy test at best.
People like to say, oh no, these things are nearly 100% accurate.
But here you've got substantial difference between a local test and a central test, right?
If we can see that much variation in the differences, then these are not good tests for the disease.
Right.
But again, you're also assuming that the central tests were done properly and the results were
recorded properly, which we can't necessarily assume.
One last thing I want to point out on this slide is that these differences here are statistically
significant.
You don't get these results just by chance.
And then so that's kind of it.
Let me just summarize what we looked at.
We have 301 quote unquote missing subject IDs.
It's a mystery.
We still don't know why they're missing.
We do see large variation across sites.
The missing IDs, especially the ones where you have things running in sequence, missing
in sequence are concentrated at a few sites, especially Argentina.
And then we have this discrepancy in the screened and randomized subjects in the report versus
the actual data.
We have treatment subjects being less likely to have local tests done.
Again, large vary unexpectedly large variation across sites.
It's not another way to say large variation.
I guess that's not right.
The right term.
It doesn't seem to be randomly distributed across the sites.
But that way it seems to be focused on a few sites.
With a change after the EUA cutoff, we have the treatment arm less likely to have a central
test before the EUA cutoff.
They were less likely than the SIBO group to have a central test done.
We have symptomatic treatment subjects with positive local tests were more likely to have
a negative or missing central test.
That was the thing that I was just showing.
And then the thing, and we go back to the very beginning before we started this, we
have this issue of process one versus process two, right?
They tested on process one and they gave everybody doses made from process two with a higher
adverse event rate among crossover placebos compared to the original treatment.
So yeah.
Wow.
Okay.
You know, I kind of get this sense there's two factions of people.
There are people who are so tired of hearing about Pfizer and their corrupt nonsense.
And we get it.
The shots are, best case scenario didn't do anything.
Worst case scenario are contributing to declining population, whatever.
Then you have the other side that finds this to be tremendously important to understand
as I put in the title, the nitty gritty of, you know, there are, for example, lawsuits
going forward in various jurisdictions around the world that require that in fact will rely
on some tiny detail that you guys are working on fleshing out and others like you.
So I fall into the camp of I want to understand exactly what happened.
I want to be able to explain it to everyone I know.
And the only way to do that is to go into, again, the nitty gritty.
So I want to start by saying thank you guys for doing this because I wouldn't even know
most people would not know where to start.
And this is something that does take credentials or long, well experienced period of having
done stuff like this before.
So I appreciate you very much.
That's what I want to say first.
I'm curious.
So you gave a summary of what you found so far and you kind of touched on what we don't
know.
What are the biggest things that we don't know yet that we clearly need to know?
That's a great question.
Not even entirely sure.
Well, one of the things is that we haven't been able to basically replicate anything
that Pfizer has reported in any of their memos.
And believe me, I've tried and Pierre has tried even harder than I have.
One of the problems that we have is that we only get the snapshot of the data as it was
on March 13th.
So if we want to recreate anything that was submitted for the emergency youth authorization,
for example, we don't have the data snapshot from November 14th.
We can kind of try to revert, go back and say, okay, we're going to stop the data.
We're going to like not consider any data after November 14th.
But even with that, we see things like people that I maybe Pierre can remind me, but you
see things like people who weren't excluded.
Then they were later excluded, even though the exclusion that they're the reason that
they're excluded happened at the very beginning of the study.
So why weren't they excluded by November 14th?
We don't know, but they weren't.
And so, you know, so you can and Pierre has come very, very, very close to replicating
the numbers on a number of different things.
But it just seems to be it's like always off.
And almost every time that we're trying to recreate something, we end up with more questions
that we've answered.
And many of those questions are nitty gritty.
I mean, if you guys think that we went into the nitty gritty in this presentation, believe
me, we didn't even get close to getting really nitty gritty.
But so, you know, so one thing is just like how, you know, hey, let's recreate one thing
that Pfizer reported to the FDA, you know, let's let's try to do that.
Now, Pierre has been in touch with Christine Cotton, who has a lot of experience actually
doing this and submitting clinical trial data to preparing, analyzing, submitting clinical
trial data to regulatory agencies.
She works with the statistical software that they use, Pfizer has released the syntax files
they used for that.
So if anybody is in a position to recreate what they've done, maybe Christine is because
she has the actual code that they used.
But Pierre has been in touch with her trying to recreate the code, you know, in a Perl-friendly
format and still, you know, they're still having trouble.
And by the way, there's a lot of work that Pierre has been doing with Christine or consulting
with Christine that's on his sub stack that we didn't even have a chance to go into.
So what are, Pierre, maybe you have a thought on what are some of the big, big picture?
In terms of what do we need, I would answer very simply a forensic copy of the database
server, that's the smallest thing we can have to reach confidence and clarity in this data.
And I'm not expecting that it will achieve confidence if we get our hand on it.
Briefly, we know more or less what the sub testing of the subjects has generated as bias
thanks to the visit-free antibodies testing, which were made and which are reflecting a
much higher part of infected people in the BNT group than what was accounted in the study.
Jekyllix made an article about that a while ago on the other expectation.
But back to the subject's direction, the fact that we strongly suspect that subjects have
disappeared at a given time of the study, the fact that we don't have data with accurate
timestamp allowing to reproduce the analysis per the ADLG, and the fact that they did themselves
mistakes in the data that we have documented, for example, not excluding at EUA time people
who hadn't received those one, introduce a number of potential bias in reproducing the
analysis that I doubt that everyone has ever done it, including at the regulator side,
which should have been done as far as I know.
First of all, the data is just so messy and complicated.
There's just no chance that people at the FDA did a real thorough job.
For example, there's a August statistical review memo from the FDA where they look at
the adverse event rate of the placebo group that crossed over and got the jabs.
They show that that rate is lower than in the original group, which contradicts the
memo that Pfizer submitted to them.
Actually, this might also be a memo.
I don't know, but they didn't control for observation time or exposure time, the person
years.
The placebo group was only under observation for a short period of time at the data cut
off compared to the original treatment group that was under observation for a much longer
period of time.
You have to control for that.
Now, if you don't control for that, then you don't see that it's a higher adverse event
rate, but when you do, you do see it.
In this statistical review memo that the FDA is going through very, very carefully and
whatever, they completely missed this basic fact, this basic issue.
Just to go back to what Pierre said, yes, this issue of the missing subjects I think
is really crucial and just a huge mystery.
I emailed Peter Marks about it.
He forwarded the email to somebody under him in the chain of command.
She emailed me and said, we'll look into it.
It'll take some time.
I emailed her back a week later saying, okay, great.
Do you have any idea how long it'll take?
I haven't heard back, and that was several weeks ago.
How long will it take for you to review the original trial report?
Yeah, right, exactly.
Or just to give us an idea of how this could happen.
How could you have all of these missing subjects?
All they could... I also submitted an inquiry to ICON, the company whose software was used
to run the trials to see if they could explain this glitch.
I didn't hear anything back from them at all.
It would be great to get some clarity on that.
I'd like to take a crack at summarizing all of this.
Okay, great.
Definitely.
Before this, I just want to point out something that several people that I've talked to who
have worked in BioTech or have worked in the pharmaceutical industry have told me, which
is that Pfizer is not much of a drug development company anymore.
It's almost a little bit weird for Pfizer to be controlling trials because more of their
business model over the past couple of decades has shifted toward being a supply chain management
company and a regulatory interface corporation.
Somebody can correct me if I'm wrong about that, but relative to the size and scale of
their business, they don't do a lot of trials.
When people have looked in, those trials have been less than scientific looking.
Right.
There is somebody, I think, who goes by the pseudonym, let's say Richard Kogan.
He's always tweeting people that it's not Pfizer, it's BioNTech.
It's not Pfizer, it's BioNTech.
It is true.
The study sponsors BioNTech.
What you're saying, it kind of makes sense that Pfizer would partner with a drug development
or the drug developer would partner with Pfizer to be their regulatory interface and to help
with the supply chain and the production and everything like that.
That level of compartmentalization, I think, encourages the compartmentalization of tasks
that allow for the potential manipulation of science.
But I'm going to summarize it like, I'm going to summarize all this like this.
If there is no notebook, did science take place?
If you can't reconstruct the process, if you can't follow the chain of data, if
people can't answer your questions 2.4 years later, if you can point to a part of the process
and ask a question and don't get a simple answer, then there is no universe in which
that trial report should have been accepted as conclusive on December 10th, 12th, 2020?
11th?
We'll take the average.
Yeah, there we go.
The entire notion of this being good science is total absurdity.
And thank you, thank both of you and the many other people, the Brooke-Jackson's, the people
who have looked at this on every level, the J.K.
Leakes, the mouse army, but everybody who has done careful work to shine a light in
so many corners that there really just isn't a good argument.
There is no trial notebook.
Science has not yet taken place, at least so far as has been communicated to anyone
in the public or any of us.
Now I'm curious.
I had a, I'm in multiple task force type groups trying to figure out, you know, public relations
type stuff, you know, to try to get people just in the general public aware of some things.
I'm in legal task groups trying to get the right information into the hands of the lawyers
in various contexts up here in Canada and further.
And first of all, there's someone I want to connect you guys with because you may be able
to help, but more generally, or rather on the bigger, more well-known case, you've alluded
to Brooke-Jackson and her experience at the Ventavia clinical trial site that has become
a very well-known story.
Now of course, in recent weeks, her, her key Tam case was dismissed.
And that doesn't mean it's the end of the road for that effort.
It does, it does complicate things a little bit.
There's some nuances to whether this was the better of two bad outcomes.
The alternative being that the Biden administration steps in and shuts it down themselves, which
was on the table.
My question I suppose is this all, I assume you guys have been working to some degree
with Brooke and with, with her counsel, with Warner Mendenhall, Robert Barnes.
Are you able to talk about any of that at all or, or, or confirm or deny?
You know what?
I actually had a conversation with Warner.
Pierre, I forgot to update you about this.
I'm sorry.
I had a conversation with him last week.
I went through some of these key, some of the, just very briefly, kind of like summarizing
some of these.
It's, it's, it's too, it seems to be maybe at least for him, what he's looking for just
too deep in the weeds at this point.
He says, I like, I like, I like simplicity.
Like I, you know, you want, these are, you know, each one of these things in and of itself
is not enough of a smoking gun, right?
And the, it doesn't get to a smoking gun by the sum of its parts, at least I think as
a, from a legal perspective.
No, I don't know.
I don't know.
But that, that seems to be his inclination.
If we were ever able to make some kind of strong, decisive claim about these missing
objects, I think that would be.
The smoking gun is that there's no science notebook.
Right.
But you can't sue, you know, I don't think they can, they're not going to be able to
make a legal claim on that basis.
I think.
But it, which is interesting.
I mean, it is, we should be able to make a legal claim on that basis.
I mean, the fact of the matter is that that's totally irresponsible science.
I mean, that's like, you know, if you have a restaurant, you don't have, I don't know,
someone come in and examine the kitchen, right?
Right.
There's no, there's no regulatory approval of, you know, we don't know that the kitchen
has been cleaned ever, right?
Right.
Or here's what you're taking, you're, you're, you're making the, that determination based
on pictures that the restaurant owner took of their kitchen and sent it into you and
said, Hey, look how clean our kitchen is.
Yeah.
Or, you know, you have, I don't know.
Twitter file style.
The notebook is the process.
And if there is no record of the process, then as far as, as far as the public should
be concerned, what you have is people promoting an image that a process has taken place.
And that is the expectation in all of history of science.
And so to, to put that forth and not have that record of the process, that, that should
be, and, and, and maybe, and maybe it's a matter of talking through this linguistically
until we can state it in a way that, that helps people understand it as best as possible.
But it, it, it feels like some sort of a sales fraud job, right?
It's not the same form of fraud that we're used to.
And that's part of why it's clever.
And I bet that this is something that has gone on within the pharmaceutical industry
in the past.
And I bet that it's, it's a perfected model because nobody has ever needed to look this
deep.
And if you have the FDA going, Oh, you know, this is all, this is okay with us because
they see a well-typed set trial report.
You know, a well-typed set trial report is not a science notebook.
Right.
But there's the illusion that these things are similar, if not the same, they are not
similar.
They are not the same.
We're coming up on the end of our time here and I don't want to keep you guys over.
I have two more questions I want to ask.
The first is it's interesting as we go through a presentation like yours, or we hear from
you for the first time names like Ventavia and Icon and for some even BioNTech.
Because we do think of this as the Pfizer vaccine.
Some people add the BioNTech at this point, but it occurs to me the, and, and this speaks
to it.
We were, I asked about Allmac and where they are in the system and what's their role in
the whole thing.
There are so many different organizations, both, you know, corporations, government agencies,
various types involved in this entire process, not to mention the principal investigators
at each of the sites.
And then you could go all the way down to their teams and lab assist, whatever.
But even just looking at the top level of just like, I'm wondering if anybody has ever
put together sort of a rough org chart just to be able to visualize.
And we're talking about this clinical trial as a whole.
This is what it looks like.
This is this map of the players involved.
Have you guys seen anything like that?
Or do you otherwise have any, any way to frame or point people towards some, some way to
be able to articulate and understand the scope of the organizations?
Because perhaps there are groups that could look into a given organization and more.
I don't know if anyone's done that.
It's Pierre.
He loves, he loves diagrams like that.
Indeed, I love diagram, but I haven't made this one so far.
So it will be one of the two things to be done quickly.
Another thing I have in mind is that we should do a map of the frauds observed in this trial,
meaning that on each site, it's not the same way to cheat.
And it may be amusing to have a visualization of this South African site at nine placebo
cases, but at three subjects disappeared and six subjects invalidated from efficacy, for
example.
Interesting.
I'm looking for ideas to, how to get people interested and how to get the argument simple
enough.
Right.
That's, that's the problem is making, keeping it, keeping it simple.
Okay.
Well, my, my last question is, um, what, what do people have wrong?
And maybe there's a horrible question to end with, cause this could, I'm sure go on for
a long time, but what, what, what do people believe that is incorrect or what is something
that is like a pervasive misunderstanding in our circles in general about the trial
or about what you've just gone through?
What's something people believe that isn't true?
Okay.
So one that I, that, that comes up again and again, and, and I, I believe that they've
actually corrected it, but, but, um, uh, Daily Clout and Naomi Wolf's organization for a
very long time, Naomi Wolf was quoting, um, miscarriage figures.
And at some point she was saying that these were people in the clinical trial that there,
there were these pregnant women in the clinical trial and high rate of them had miscarriages,
which there was a confusion there because they were taking a document that was based
on post-marketing adverse event reports.
Post-marketing means after they started deploying the vaccine, um, to millions of people, they
were getting, there were adverse event reports and Pfizer was required by, had a regulatory
requirement to gather data from a variety of sources, including VAERS, including UDRA
vigilance, including those kinds of things, including people calling up Pfizer and reporting
problems with the vaccine and taking, and then reporting to the FDA about what they
were finding.
And so the numbers of pregnant women were not in the trial.
Um, they were from this data set, which has all of the different biases and drawbacks
of all of the spontaneous reporting data systems.
And so you can't calculate a miscarriage rate because you can't calculate a denominator.
Even if you know how many doses were distributed, you don't know how many were taken.
You don't know how many were taken by pregnant women.
You don't know what week the women were pregnant, what week of pregnancy the women were in.
You don't know the under reporting rate.
And so you don't actually know the number of miscarriages that actually occurred, et
cetera, et cetera, et cetera.
And so it stands to reason that if a woman is pregnant and she takes a vaccine, she's,
it's more likely to be reported as an adverse event if she had a miscarriage.
Now there are many of some of the adverse event reports for pregnant women is just,
we gave this to a pregnant woman.
That's an adverse event.
Nothing bad has to happen in order for them to report that.
So, so they, you know, you, you get these high rates of like 82% of the pregnant women
in the trial had a miscarriage.
That's just wrong.
And so, you know, it's, it's like, it's so wrong at so many levels and you see it repeated
again and again and again.
And even though I believe in the, the, the, the book that they published, the report on
that, I believe they got it right in that final report, but it was repeated so often
prior to that wrongly that that's what's, that's what's stuck in people's head.
And I see that repeated.
It makes my blood boil.
You know, so daily cloud had, they, they have like a bunch of different reports that they
were releasing throughout their study of the trial.
And then they put a bunch of them together and published it.
You can get it on Amazon.
I forget what it's called.
You can go to the date.
If you go to the daily cloud website, I'm sure there's an advertisement for it.
Okay.
Thank you.
Yeah.
It's, it's this guy.
There you go.
That's it.
Yeah.
That's it.
Yeah.
Very interesting.
This is another interesting thing we could dive into, but what, what about you, Pierre?
Do you concur or is there something else that you've, you've identified for your,
I concur on the most regular error that I noticed around me is that people are systematically
telling me that the real world data has proven since the trial that the vaccine is safe or
effective and there is nothing more wrong.
Yeah, so far we haven't seen a single satisfying study with data, which allows us to conclude
that aside from trusting the FDA, and I believe we have covered why we should have questions.
Right.
Like who cares about the fires or results?
We know from, you know, from all these other studies after it was released that it's safe
and effective.
Well, I've got a dead friend who may beg to differ.
Well, on that note, going off on a high note, again, you guys have done tremendous work
and it's ongoing work.
I'm always very pleased when I get the new, from both of you guys, the new substack emails
into my inbox and you do make it, despite the inherent challenges in communicating this
information, you do make it easy to understand.
You do add context, you do add links and references to other things if people do need more context
or need a recap.
You link to each other's work, you link to other people's work.
So, so thank you.
Thank you for this.
Thank you for the ongoing effort.
And I highly encourage again, everybody to follow both, both substacks, which I'm going
to pull up one by one.
On our way out, Pierre, where can people find you?
What are your social media handles?
Where do you want to direct people to find more of your work?
On Twitter, I'm on my current sixth account on Canceled Mouse and on Substack, I'm OpenVAT.
Did you say your sixth Twitter account?
Yeah.
You've had to go through six.
Yeah.
I was banned for banned evaluations since I was anonymous.
You don't need to justify your ban, you know.
So publishing open data and code is dangerous nowadays.
Pierre is at Canceled Mouse on Twitter in case somebody, anybody didn't catch that.
Oh, that's you, Pierre.
Okay.
That's me.
We've been communicating recently and I was enjoying our communication, but I hadn't connected
the two of you yet.
It's not easy to keep track.
And now it's not about me anymore.
Pierre, you're also one of the only people who engage us on, I think, either Gab or Getter
is where you'll retweet or regab our stuff.
I stopped to use Getter because I was shadow banned constantly since I published an analysis
of US census data, which is deeply flawed.
Shadow banned from Getter?
Yeah.
Okay.
Well, this may have something to do with rounding the news episode that I'm doing this Friday.
So there's a fun little cliffhanger teaser for everybody.
Very interesting, Pierre.
I'm sorry to hear that.
No worries.
Okay.
Well.
Interesting detail.
You can see in code who the shadow ban is, if you're interested.
Okay.
Well, very interesting indeed.
Now how about you, Josh?
Where would you like to direct people to in addition to your fantastic substack?
Thank you.
I'm at JoshG99 on Twitter.
That would be the other way, retweeting lots of stuff.
Fantastic.
Okay.
Well, now I'll just do our plug here.
I highly recommend if you haven't been following us over here on locals, runningtheearthotlocals.com,
we've been having excellent community discussion in the locals chat as we always do.
And if you're not yet a member, highly recommend becoming so in order to keep track of everything
we're doing in this nice, easy, friendly, community-based format.
And if you want to support Running the Earth financially, you can do so for as little as
five bucks a month and you get access to super sweet, super exclusive weekly live streams
where we talk about stuff that we're not quite ready to put out into the main public sphere,
but within a smaller internal community brain trust, you might say.
We always have fantastic discussions.
So runningtheearthotlocals.com.
And yeah, that's our plug there.
Matthew, any final words before we kick ourselves out of this internet video stream?
Yeah.
I'm just going to say it again.
No notebook, no blueprint, no science.
As far as your trust at least in whether or not science has taken place, no notebook,
no blueprint, no science.
It's not communicated as far as we know it's a black box.
And that's not acceptable for the largest experiment in human history.
That may be acceptable for learning how to bake a new cupcake, but it's nothing on this
grand scale.
It's ridiculous.
And thanks to Josh and Pierre and all the people who have done detailed work to help
make that plain because you have to shine a lot.
It's so big that you have to shine the light in a lot of places to get to the point where
you can make that statement.
No notebook, no blueprint, no science.
Thank you for giving us the platform to share our work.
And you're welcome back absolutely anytime.
I know there's going to be further updates probably sooner than later.
And we had this one scheduled like a month ago.
So I'm sure we'll be able to get you back on even sooner than a month from now, if so
required.
So thank you again and thank you to everybody who tuned in today.
Yet another fantastic show.
And yeah, I look forward to seeing everybody.
Well tomorrow for our weekly locals exclusive and then I'll be back on Friday for as we've
teased here something a continuation of last week's show.
And it is interesting that this whole getter discussion has come up because it is directly
related to what I'm going to be talking about.
So there's your little cliffhanger.
We'll see you guys again very soon.