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Debunking Viki Male: Part 2 - the Calvert Affair
The biggest study yet showing COVID vaccine "safety" in pregnancy turns out to be a Surgisphere rerun
Those of you who were paying attention would have noticed that the previous article ended with a “there’s more” kind of bait… not only because there is “more” but the “more” is the juiciest in Viki Male’s punch of disingenuous research papers that falsely reassured pregnant women that the failed COVID mRNA “vaccines” can be given at any stage in pregnancy - just like thalidomide could (until we eventually saw it was killing and maiming babies).
The paper that stars in this sequel is this one and of course is in one of our favourite Pfunded-by-Pfarma journals, Nature (which used to be the ultimate scientific journal of high regard).
The study is ostensibly a comparison of matched cohorts of pregnant women - vaccinated vs unvaccinated. Great. Just what we were looking for. It’s sounds like what the V-safe registry should have provided when they had access to potentially tens of thousands of unvaccinated pregnant women who would have happily signed up to the control group for V-safe, but weren’t allowed to.
The Calvert study is huge - over half a million pregnancies worth! Surely it must give us the answers we are looking for, because it’s written and analysed by the “official” NHS Scotland government. So it must be McKosher. Well, let’s see.
The first thing that stands out about this huge study is the peer review. It’s eloquently and very briefly described in the Nature documentation. Read it for yourself:
You might need to do a double-take to believe what you’re reading but, yes, it does say that this huge study was not peer reviewed. They just published it, whether it was bullshit or not. Great work if you can get it.
And here’s the supervising author telling us all about her previous wonderful study telling us how bad COVID was in pregnancy. So you’d better get your vaccine (the one that doesn’t prevent COVID)
The following things are clear from this video (dated 31st Oct 2022, when it was well known that the “vaccine” didn’t prevent COVID):
She is reading from a script
She is happy to lie about safety and effectiveness “at any stage in pregnancy”, without turning a hair
She is grifting on the base rate fallacy, claiming that “90% of women who were admitted to hospital with COVID-19 were unvaccinated” failing to point out that was mostly because the pregnant population were generally unvaccinated at the time of the study and pregnant women were preferentially directed into hospital if they were unvaccinated (for their safety, of course). This created a huge bias in their previous study, which she is falsely suggesting as proving effectiveness of the ”vaccine”.
The trick she is pulling is part of the bait-and-switch relating to providing information to pregnant women. It works like this:
Provide two “facts” about vaccine safety, without the facts being verifiable.
Provide “facts” about the outcome of COVID in pregnancy, making it sound worse than it is, without the facts being verifiable.
Suggest - without declaring explicity - that the “vaccine” prevents the “outcome”
The Chief Medical Officer (Gregor Smith) used the same tricks in his dictat telling all doctors and midwives to advise pregnant women to get the COVID “vaccine” here.
And this was the same heinous medical officer that banned “unvaccinated” women from having access to fertility treatment in Scotland.
So given the people concerned (Stock running the study and Gregor being in charge of NHS Scotland providing the data) it comes as no surprise that you can probably predict the outcome of the Calvert study without even needing to read it. But we are going to dig in anyway.
This study is the archetype of why a retrospective study is often dismissed as irrelevant. Here’s the CONSORT diagram showing how patients were selected. I have just picked one of the many of these in the paper, as the selection criteria for this study is so deliberately confusing it has red flags all over it. For reference, the supplementary can be found in the footnotes.
So, of a total of 400,000 “prepandemic” pregnancies only 9,075 were selected to be included as a comparator group. That’s roughly 2%. That’s completely unacceptable and absolutely subject to abuse, if for example the people conducting the study had a vested interest in making sure the study had a desirable outcome. Those cohorts drove the miscarriage analysis but there was an “ectopic pregnancy” analysis from the same data set that used a larger cohort. None of that makes sense.
Now we include another confounder in this data, as the authors excluded those infected with COVID - why? That makes no sense as part of the miscarriage pathway could relate to an increased (or decreased) risk of COVID following vaccination. This provides another red flag (see supplementary p11)
Going further into the paper another red flag arises in Table 1 which is supposed to show how well matched the comparitive cohorts were. Remember that in order to match 18,780 “vaccinated” pregnancies they procured 4x this number (exactly) “matched” historical or contemporary controls out of a total cohort of 556,167 thereby ditching 86% of the records. There was no need to do this and there is no explanation of how they chose to drop so many patients from the study to “match” the vaccinated cohort.
There are more irregularities in this data. Although the original cohort is more than 500,000 “registered”pregnancies, exclusions for terminations are not mentioned at all. There are approximately 13,000 terminations a year in Scotland such that there should be around 90,000 terminations in this cohort. There are none reported, leading us to conclude that there is a whole tranche of data missing from this dataset - assuming it is even real.
There is in fact nothing to stop this data set being totally synthetic or manipulated because the authors have not allowed it to be audited or verified. We can’t even cross-check it against NHS Scotland’s data as, unlike NHS England & Wales, NHS Scotland does not produce accessible statistics on ICD code O03 (spontaneous abortion) numbers (let me know if you find this data).
In truth it can be difficult to identify when data is junk or synthetic, but usually there will be a clue in the data somewhere. This study does not disappoint and in table 2 we see a major anomaly not understood by the authors.
So, in the ChAdOx (Astrazenaca or AZ) cohort, including in the matched cohort marked as “unvaccinated” there were (389+406+1152) = 1,947 miscarriages out of 13060 pregnancies = 14.9% (similar to the excessively high rate discussed previously). Yet in the rest of the cohort, vaccinated or unvaccinated, the miscarriage rate was (1878+1716+5566 - 1947) = 7213 out of (18780+18780+56340-13060) = 80840 which is 9%.
That’s still high for a raw miscarriage rate but the AZ cohort (vaccinated or unvaccinated) are off the scale1. Because the authors don’t understand the data that was provided to them they make this statement:
“analysis of pregnancy outcomes in 121 women who became pregnant whilst participating in clinical trials of ChAdOx1/nCoV-19 found that the rate of miscarriage was no higher in the ChAdOx1/nCoV-19 group than in the control group, with an adjusted risk ratio of 0.84 (95% CI = 0.24–2.90)16. The only other study exploring COVID-19 vaccine type and miscarriage that included ChAdOx1-S/nCoV-19, also did not show any evidence of an association between ChAdOx1-S/nCoV-19 and miscarriage (aOR 0.84 in vaccinated women compared to unvaccinated women; 95% CI = 0.48–1.48)8 . On balance, the evidence relating to ChAdOx1-S/nCoV-19 and miscarriage is reassuring”
…suggesting an attempt to rationalise this massive anomaly by discussing the comparative rate of miscarriage in the vaccinated vs unvaccinated AZ cohort - which is irrelevant. There was no difference in rates between vaccinated and unvaccinated in the AZ cohort. The anomaly was that the AZ cohort were allocated a much higher risk of pregnancy than all the other cohorts, whether vaccinated or unvaccinated. In fact this anomaly was so strong the probability of it being a chance variation was statistically zero (chisquared test, p=1.6e-101)
Because this was almost certainly due to a selection algorithm the anomaly shows that this data is corrupted and needs analysing and verifying by people who understand it. Not a regurgitation of something that a level 2 data analyst who has never met a pregnant woman spat out. This one anomaly is enough to disqualify the whole data set, because the production of it is a “black box” just like much of the data that comes out of the UKHSA that we are also not allowed to see. We are only allowed to see their curated and cleaned version. Which reminds me of this.
Even more tricks
The paper gets murkier the more one reads into it. Instead of submitting a new ethics (HREC, IRB) approval, Colin Simpson - one of the co-authors - appears to have requested an amendment to an old ethics approval for investigation of a 2009 swine flu vaccine effectiveness study. This (Calvert) study is so far removed from that one it’s not funny and no way should have been an “add on” to the ethics approval. So it seems that these authors are not shy of cutting corners.
Intriguingly, Sarah Stock and Colin Simpson just happened to team up in 20202, ready for the COVID vaccine push - what are the odds? Add in to that mix the fact that Clara Calvert and Sarah Stock teamed up in 2021 and started pumping papers out left, right and centre it seems that the papers from the group have an air of having been ghost-written like the Lancetgate Surgisphere papers.
Worse, Colin Simpson - who now has a Professorship in New Zealand (presumably gatekeeping their vaccine data because some of it got out to the public when it shouldn’t have) - prides himself on his “HDRUK Impact of the year award 2021”. Yes, that HDRUK.
Surprisingly (not), the same group also published in Nature Communications this year using the same data set and the same methodology and showed that there was no increase in congenital anomalies following COVID vaccination when compared against a “matched cohort”. Hmmm.
The rate of congenital abnormalities after vaccination was quoted at 2.3% but this is surprising as the v-safe pregnancy registry data showed a 3.5% risk of major congenital abnormalities, which is much higher than the background rate (which should be less than 2%).
However, the CDC themselves in the Zauche presentation quoted a background rate over 3%. So is this data set synthetic or have Calvert and Simpson managed to find a super healthy cohort of pregnant women in Scotland?
That is, they are created from harvested (ethically or otherwise) “Real World” data and tweaked to produce a data set that tells a specific story.
An AI-generated data set if you would, that suits the narrative.
Given that both these data sets are effectively acting as pharmacovigilance data for a black box drug that was only given a conditional licence (conditional on pharmacovigilance data being monitored) they should not be locked up away from inspection.
Again, and very suspiciously, the Calvert - Simpson data set showed that the Astrazeneca vaccine (taken by very few women in pregnancy given that the advice was to take mRNA vaccines in pregnancy) was taken by a large cohort and showed a 1.5x risk of non-genetic abnormalities, much higher than the mRNA vaccines which didn’t show any increased risk.
Convenient eh? No, it’s not possible.
We now have two papers from the same authors showing that the Astrazeneca vaccine (which nobody took in pregnancy and is no longer available and in any case was thrown under the bus in favour of the mRNA vaccines) showed weird anomalies in regard to miscarriage and congenital anomaly. Or perhaps, this is what Simpson’s AI generated because it was instructed to.
The murky world of Sarah Stock
It gets more interesting. The “corresponding author” for these (NHS Scotland aka COPS) papers is Sarah Stock (pictured above), an obstetrician and Professor at the Usher Institute in Edinburgh. She is also supposed to be at the University of Western Australia, because it’s so easy to be in two places at once.
It’s a red flag when the first author isn’t the corresponding author, because the first author should be the one to answer any questions about their own paper.
When the first author isn’t the corresponding author it is sometimes the supervising (last) author (e.g. if the first author is a trainee that might be moving around). In this listing Sarah Stock is not the last (supervising) author - that’s handed over to Rachel Wood and they swap in the two papers.
It’s odd. And it’s a red flag because it suggests that Calvert needs a minder.
In fact this minder (Sarah Stock) is quite a big wig in the world of pharma because she is the director of the insidious sounding “In Utero” program at Wellcome Leap (yes, that Wellcome which runs the international slush fund for Big Pharma) and which was exposed in 2021 by Whitney Webb.
So, we have the director of a creepy Wellcome initiative working as gatekeeper for the Scottish pregnancy vaccine dataset, along with another gatekeeper self-affiliated to HDRUK and who seem to struggle with whether or not to declare their conflicts of interest. Surely that would be reassuring that the data is clean?
Of course that’s not the end of it. Remember that the first author is Clara Calvert. You’d think the first author would be an OBGYN or at least a medical doctor, because of the nuances of looking at gestation data?
Nope. Not Clara. She’s a PhD from the London School of Hygiene and Tropical Medicine (part of the IRIS research network which disparages anybody that counters the vaccine narrative) who is now at the same Usher Institute as Sarah Stock. As she isn’t an OBGYN her contribution presumably is that she is an epidemiologist/biostatistician and therefore must have run the analysis, analysis and verification herself.
Well, apparently not. Because if you go into the github repository for this analysis you see Jade Carruthers, Leanne Hopkins and Lisa Hopcroft, who are co-authors that work for Public Health Scotland.
In other words, these three researchers provided the data that Clara Calvert published as her own.
Now, I’ve been around data science a while and data scientists (the ones who procure the data using SQL or HL7) don’t in general look like these three - they tend to be geeks who spend a lot of time on computers. So, although it’s not impossible my guess is that the three amigos here are “data shifters” or “data cleansers” and were responsible for uploading cleansed data somewhere that the analysis (published in the github repository for “transparency”) can run on.
To clarify (because it’s easy to get lost in this)… below is the kind of code that’s in the repository. The code is published for “transparency” but is completely useless because the .rds data file (presumably cleaned by the cleaners) is hidden from public view/audit. The code is simple enough and written in R but is downstream of the cleansing process.
In other words, the raw data can’t be seen, nor can the scripts (.R program files) that clean the raw data.
This is what we call a “black box”. The public has no way of seeing what filters or shenanigans were applied to the data, to make it “clean”. It’s data laundering 101.
The bottom line with this is that you just have to “trust” that the data that these people have produced is real, and hasn’t been manipulated to meet a narrative.
Well, all fine and dandy but we’ve already established that the supervising authors have undeclared conflicts of interest and vested interests in making sure that these vaccines look super safe while miscarriages are rife.
Clara Calvert is pretty much incognito in the medical field and the other data scientist here - Lisa Hopcroft - managed to get a tidy promotion with the infamous HDRUK/Opensafely, which is Ben Goldacre’s Wellcome-funded organisation mentioned earlier and repeatedly produces data that can’t be verified but definitely toes the party line.
So let’s recap and see how reliable we think this data is
It’s supervised by the director of the obstetric subsidiary of Wellcome Leap, Pharma’s creepy slush fund project
You can’t ask the first author about their study, you have to go through the Wellcome Leap gatekeeper
The highest miscarriage rates are in the Astrazeneca cohort, whether they took the vaccine or not, and the mRNA cohort were absolutely fine. No problems at all.
The people that created the data sets from an enormous database look like they just got out of school.
You can’t ask for the schoolies’ data, because you’re not allowed to look at it.
The other major player involved in this data set got an award from Nicole Junkermann’s organisation.
The data used in the study was a tiny fraction of the available data, and was hand picked.
This group is pumping out multiple studies in a short space of time that appear to be ghost-written.
I’ll there be giving the Calvert study the highest Ark Pants-on-fire rating of “catastrophic”, which means…
That the best of Viki Male’s research papers, claiming that the COVID vaccines are safe in pregnancy, is not just unverifiable but likely synthetic.
If the best of her collection of papers is junk, the whole collection is junk.
The involvement of Stock, Simpson, Calvert and Gregor Smith have not only clouded this data set with the stench of political interference, but has brought the whole cohort of papers under scrutiny. If this paper turns out to be synthetic (fraudulent) like the Surgisphere studies, it could - and should - blow open the whole murky world of pharma-funded medical research institutions.
The supplementary data for this paper is archived here for reference