Debunking Viki Male: Part 1
All the papers that Victoria Male uses to promote the "safe and effective in pregnancy" mantra are junk, without exception. Here's the breakdown.
Yes I know I’ve been busy for a few weeks - for various reasons - so hopefully this longish article will make up for my absence. Although the hiatus was partly due to the need for a break this particular article has required more than the usual amount of research and the web of deceit uncovered reaches into many corners of medicine. I have had to decide how far to take this story and how to present it, because it really is the archetype of how corrupt our medical-pharma complex has become. It’s so long I’ve had to split it into two parts for readability.
The star of our show today of course is Viki Male, pharma’s golden girl who was rolled out in 2020 to be the spokesperson for all the scary things that can happen to a pregnant woman if she dares to get COVID, so that all pregnant women by 2021 were coerced into taking an experimental gene technology vaccine of a type that had never been given to pregnant women before. We have indeed discussed Viki many times and I suspect she is a bit fed up of this particular thorn in her side.
In order to promote these pharmaceutical products on the pregnant population Viki not only has the whole world’s media complex behind her but she has been prominent in “established medical journals” such as the infamous NEJM (of Surgisphere fame).. who have recruited her to write editorials telling us how safe the untested COVID vaccine is in pregnancy.
Obviously this wasn’t enough, because it wasn’t enough that 50% of the pregnant population got conned into taking a gene therapy vaccine during pregnancy. No, whoever is instructing the Vikster wants 100% compliance. So Viki wrote a dynamic document which she posts on her twitter feed and is updated at regular intervals and that is the document used to propagandise the “safety” of COVID vaccines in pregnancy - Für Ihre Sicherheit.
We’re going to show that it’s abject propaganda of course and the purpose of this article is to expose it. The first part (this one, obviously) takes on most of the papers in her list, but one special paper - the Calvert paper - is reserved for its own article (yes, it’s that bad).
Here’s the TLDR for those of you with no stamina:
Every paper in Viki's collection that relates to the miscarriage risk following COVID vaccines is compromised by undeclared conflicts of interest, declared conflicts of interest, confounders, bias or unverifiable data.
There is not a single randomised trial or a single retrospective study of value for which the data can be verified.
In short, Viki's papers are junk. Every last one. For those who want a direct link to the paper you can follow her google link here but you might want to use an incognito window to avoid your identity being tracked. Alternatively the list of papers from the version on which this article is based is added in the footnotes1.
For those of you who want to get to the bottom of this I suggest you grab a coffee, glass of wine (large) and get comfy on the sofa.
We’ll be going through the papers one by one with the most important papers first apart from the Calvert study - Viki’s apparent coup de grâce - which takes pride of place in part 2.
1. V-safe pregnancy registry (Shimabukuro 2021, Zauche 2021)
https://www.nejm.org/doi/full/10.1056/NEJMoa2104983?query=featured_home
https://www.nejm.org/doi/full/10.1056/NEJMc2113891
TLDR: The V-safe pregnancy registry papers show a safety signal for a doubling of miscarriage rate. The confusing language and unnecessary survival analyses serves to confuse the public and the data is not available for inspectionI have combined these papers because they come from the same cohort, which was the V-safe pregnancy registry that I have talked about so many times. If you have already seen the articles you will know that the whole cohort showed a miscarriage rate in the vaccinated groups which was approximately double what you would expect from such a cohort.
If you haven’t read them the best thing to do might be to start with the latest one and work backwards but for clarity I have listed them all here:
2. CANVAS study Canada, Sadarangani, Lancet Inf Dis 2022
https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(22)00426-1/fulltext#cestitle170
TLDR: Red flags in this paper include the huge list of conflicts of interest, significant confounding by mismatching gestations and maternal age between groups, ridiculously short follow-up and confusing plots. All hidden behind a shield of gatekeeping.This is a shockingly bad paper out of Canada, but of course published in the Lancet. It’s worth remembering throughout this substack that a lot of papers here are published in either the Lancet or NEJM.
Both of these “journals” are conduits for Pharma, and both were utterly discredited in the Surgisphere debacle where they published obviously fraudulent data… and we caught them (if you haven’t seen this excellent video from Chris Martenson I can’t recommend it highly enough, especially as it features my prior twitter account…)
Back to the CANVAS paper…
The first red flag of this paper is the conflict of interest section. It’s so long it’s almost longer than the results section. There is not a hope in hell that you are going to get this group to publish a paper showing any risk from the COVID vaccine at all. Ever.
And if you think you can have a look at their data to see if they cheated - not a chance. Their data sharing agreement is shrouded under gate keeping meaning that you have to apply for the data in an identified format and following submission of a proposal that you can’t deviate from, and there is no chance at all they will let you publish any findings that are discordant from their narrative.
The next read flag is the fact that they only allowed 7 days of follow up. So if you had the vaccine and then had a miscarriage 3 weeks later, you wouldn’t be counted.
They also did this weird thing in the results section where they switched over the groups in the forest plots to confuse the reader. So although it looks like some things were better following vaccination, all outcomes were worse.
In fact they were so much worse that overall mRNA vaccination was associated with an odds ratio for the risk of significant health events of 2.4x - more than double.
Another trick that a lot of these groups pull is called “skew the gestation”. To play this game the control group has, say, 70% of the participants in the first trimester (week 6-13) and 30% in the other gestations (week 14-40). The treatment group then might have a reversal so 30% first trimester and 70% late pregnancy. Because 90%+ of miscarriages occur in the first trimester, failing to match this distribution will immediately show your drug to be safer than it actually is. In case those pesky researchers try to see if you are playing this game they sometimes play “hide the gestation” where it is impossible to work out what proportion of women there were at each week of pregnancy in each group. This is the game that the researchers played here, and they even admit to it.
Despite that, there is a clue that the trimesters are not matched in these groups. We can use their “health events” (events occurring requiring medical assessment) as a surrogate - assuming they occur at a constant rate in pregnancy. In the unvaccinated group there were 6 out 11 (55%) of these recorded in first trimester. For dose 1 this was 36% (81/225) and for dose 2, 29.5% (67/227). Although the number of events in the unvaccinated group was small there is clearly a higher proportion of first trimester events in the unvaccinated cohort than in the vaccinated cohort and therefore it is likely that the miscarriage rate in the vaccinated cohort was underestimated (because there was a lower proportion of fewer first trimester women in the vaccinated cohort).
Another confounder in this paper is the the fact that the control group (unvaccinated) were older, with 85% in 30+ age group (288/339) compared to 75% in mRNA group (4180/5597) [p<0.0001]. The p-value indicates a level of significance that the groups were unmatched sufficient to alert the reviewers, but of course it didn’t.
These confounders were easily enough to hide a doubling of miscarriage rate and therefore without an independent review of the data we can safely say that it did not show what the authors claimed.
3. Kharbanda, JAMA, 2021
https://jamanetwork.com/journals/jama/fullarticle/2784193
Ostensibly this is one of Viki’s show papers, such that it’s one that is referenced by the Pharma pushing brigade often. It’s actually not a paper at all but a letter to JAMA.
What’s worse is that it is a hotbed of conflicts of interest. These are the conflicts declared in the letter itself:
Remember that Heather Lipkind was actually paid to be on Pfizer’s DSMC2 - and so the use of the word independent here is an outright lie. The paper itself is actually from a series of papers that were salami sliced from effectively the same data set, the source of which we’re not allowed to see - obviously, because gatekeeping.
On a related paper the conflicts are even worse. This whole authorship group is tainted by a relentless stream of pharma funding from vaccine groups. There is not a cat in hell’s chance that you are going to get an “independent” study from these people.
If you ignore these massive conflicts of interest and dig down into the data itself you are going to end up in a hole that you won’t be able to dig out of3. The data is split 4-weekly sections such that any individual in the study can end up represented more than one time in the denominator (total pregnancy events) but not the numerator (miscarriages). So that means that we are going to end up massively underestimating the miscarriage rate.
From the table it looks like there are 250,944 pregnancies and 13,160 miscarriages giving a miscarriage rate of 5.2%. But because these are “ongoing pregnacy periods” there were actually only 105,446 pregnancies giving a miscarriage rate of 12.4% in this cohort, which is high. Because of these ridiculous methods the following tricks are pulled:
A miscarriage should stop the number of counting periods in which a pregnancy is counted, but it’s impossible to see whether this happened
There is a big difference between the miscarriage rate at 6 weeks and 9 weeks, so it’s possible to have the vaccinated mostly in the 9 week group and the unvaccinated in the 6 week group and you would hide a doubling of miscarriage rate.
Beyond that there is also the EMR problem whereby the data is gleaned from an unknown EMR system from which it cannot be interrogated or verified. There is nothing to stop a selective bias being implemented in collecting data from different risk groups for miscarriage as confounders. And there is nothing to stop data which didn’t match the narrative from not being collected. Because we can’t see the original data, you just have to “trust Pfizer”. Not good enough.
Even on the basis of the little data that is available here there is a red flag in the miscarriage rates per 4-week period, which varies considerably depending on the reporting time between December (when few pregnant women were agreeing to COVID jabs) and May (when many women had been coerced to receive them). During this time the miscarriage rate per reporting cohort went from 4.8% to a high of 5.7% and there was a very significant difference between this miscarriage rate in the first cohort vs the rest of the study, as cannot be expected by chance (p<0.0001). The obvious explanation for this is that as the vaccine was rolled out the miscarriage rate increased, but the authors didn’t bother to look at this.
It should also be noted that Heather Lipkind was the supervising author on this paper, which normally means that she is the head of the lab producing the research or that she has supervised this particular paper for Elyse Kharbanda. It’s a bit odd because Kharbanda should be a supervising author given her history. In fact, Lipkind seems to be acting as gatekeeper for this study, making sure that Kharbanda doesn’t step out of line. Lipkind was exposed recently in this deposition by Aaron Siri which I encourage everyone to watch. It’s very revealing about how little she actually knows but it is clear that she is simply a puppet for Pfizer.
(For further reading on the DSMC members’ conflicts of interest with their payments from Pfizer please see this article.)
Elyse Kharbanda’s undeclared conflicts are no better, with financial ties to Fauci’s NIAID and NIH, co-sponsor of the Moderna mRNA vaccine about which the paper was partially written. She just forgot to declare this in her conflict declarations, because - I don’t know, NIH or something.
Furthermore the data analysis has a definite stench of Surgisphere about it. The data collection went up to the end of June 2021 and we are expected to then believe that a group of 7 researchers analysed the data of over 100,000 women and 250,000 reporting periods trawling through external EMR databases which are notoriously messy, in less than 8 weeks including the submission and revision of the manuscript - which normally takes 6 weeks? There is zero chance that this happened.
So the only rational conclusion that can be drawn - just like the Surgisphere data - is that the data is synthetic. Bear in mind that Kharbanda was busy writing a whole bunch of papers that year, Lipkind is far too doddery and inept to do any data analysis (based on her car crash interview with Siri) so we are now down to only 5 people available to actually do this work. Jacob Haapala was similarly busy, as was Alison Naleway who seems to be a literal manuscript robot pumping out an impossible 64 papers in 3 years.
So who the hell was it who trawled through 250,000+ records, and therefore should have been first author but wasn’t. Well if you look at the name on the supplementary it could have been this “person”:
Which looks like it may not have been a person at all, but somebody who either had some help from AI, or is actually the front name of an AI bot, which (to date) is not allowed as the author of a medical manuscript. If you think this is an imaginary concept, think again. Check this out - it’s literally an AI version of the infamous COVID vaccine pusher Dr Nick Coatsworth from his “proxy twin” website.
Given what we saw with Surgisphere and the very close parallels in the size and type of the dataset, as well as the intricate links to pharma, I would have to deduce that the Kharbanda data set is either totally or partially synthetic (that is, generated by an AI engine on the back of an EMR4-seeded data set).
You can read more about synthetic data for clinical studies here as well and here as well as Igor’s introduction to vaccine AI chatbots here and the definitive rundown of AI nudge units here.
4. Citu (Romania), J Clin Med, 2022
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955378/
This paper is a doozy and claims that there was no increase in miscarriage risk from the COVID vaccinations. The truth however was rather different, because it actually showed an increase in miscarriages despite a lower risk population who were younger and who had lower rates of smoking - a known risk factor for miscarriage.
Even in table 1 you can see that the vaccinated group were significantly younger (p<0.001) but the other notable factor - unrelated to miscarriage - was the fact that the vaccinated group had a higher risk of “COVID” infection (15.6% vs 8.9%, p<0.001). So much for “protecting pregnant women against COVID in pregnancy”.
The overall miscarriage rate was 13.3% (124/927, table 2) but the miscarriage rate in the smokers in this study was a whopping 28% (31/110, table 2) [vs non-smokers (11.3%, 93/817) ], so the combination of a lower smoking rate and younger population would have significantly reduced the miscarriage rate in the vaccinated population as a comparator. In fact you can see how dramatic these confounders are in the table provided:
It was unclear how all pregnancies were captured and whether some pregnancies in the denominator were not captured because they did not register at that hospital (hence creating an artificially low denominator and pushing up the miscarriage rate).
In order to ascertain whether this data was real, fake, or misinterpreted I wrote to the authors to request the data and the request was ignored.
When a data request is ignored you can immediately discount the paper. For this one I don’t think the data is actually untrue, merely that there is a selection bias in the groups that allowed them to claim an equivalent miscarriage rate that clearly wasn’t there because they overloaded the unvaccinated cohort with high risk women.
5. Ruderman, JAMA, 2022
https://jamanetwork.com/journals/jamapediatrics/fullarticle/2790805
We can dismiss this study relatively quickly as it was not really a miscarriage study. It was a study to try to quell the fears of the population in regard to the risk of congenital anomalies arising out of the result of the use of an untested novel medication in pregnancy, that we promised not to do ever again after the thalidomide scandal.
On that note if you haven’t yet watched it I would very much encourage you to watch David Mason’s 2016 documentary about thalidomide, which tells the story of just how affected thousands of babies (that then grew into adults) were and still are - and how the pharma companies, in this case owned by Distiller’s - tried to destroy the lives of the people affected in order to avoid paying compensation. Presumably their strategy also helped avoid prosecution of the criminals involved in pushing an untested drug on pregnant women because - just like with the COVID vaccines - nobody was ever prosecuted over the deaths and disabilities cause by thalidomide and its cover up.
Although this paper claimed that congenital abnormalities were not detected at a high rate in the vaccinated the study was conducted (supervised) by Emily Miller. So there was no chance that any safety signal for anomalies would be found, despite massive safety signals for the same being reported in multiple VAERS reports.
Remember that if we look back at the CDC’s V-safe pregnancy registry there was a high risk of congenital abnormality recorded following COVID vaccination such that even they could not hide it. In fact it was so high that they had to lie about the background risk of congenital abnormality being “3-5%” (it’s less than 2%) to cover up the problem.
The discordance between this EMR study and the CDC data is the type of red flag that is more relevant to a retrospective study, because the authors can essentially pick and choose which cases to include. In other words, if 50% of the babies that had congenital anomalies were from vaccinated mothers the authors could simply decide to pick up the other 50% of babies.
Because this was an EMR study (collating data from a hospital electronic record) there was actually no reliable way of assessing vaccination status or date as this was not recorded in the EMR.
The study is therefore utter junk and should never have been published without the peer reviewers (presumably chosen specifically from a pool of doctors that were running on Pfizer grants) requesting the full data set. That was never going to happen, and the authors knew it was never going to happen.
To remedy this and attempt to verify the data I request the full data set from the authors and was ignored. We can therefore safely dismiss this data as junk (or fraud).
A request for data to corroborate a medical study should never be rejected.
6. Goldshtein JAMA 2021
https://jamanetwork.com/journals/jama/fullarticle/2782047
This study - which was not primarily intended to look at miscarriage rates but COVID rates - can be immediately dismissed as suspicious for being fraudulent (or misrepresenting truth5) due to the stillbirth figures quoted.
The best stillbirth rates in the Western world are 0.3%, or 3 per 1000. This has increased recently following a steady trend to reduction. The population here is 15,000 women so there should be a minimum of 45 stillbirths. There are 3. The probability of this being by chance given the expected rate is less than one in a million.
This paper was primarily investigating COVID infection rates in vaccinated vs unvaccinated cohorts and, now that we know that the infection rates are higher in the vaccinated6, it is somewhat of an eyebrow raiser to see that the authors concluded that the COVID rate in the vaccinated group was 42% lower.
Part of the problem in this paper is that there were only 28-70 days of follow up and this may explain the ridiculously low rate of stillbirth and miscarriage events, with miscarriages being reported at the impossibly low 1.7%.
The paper can therefore be assumed to be total junk. Funnily enough the lead author Inbal Goldshtein declared no conflicts of interest on the paper yet just prior to COVID published as first author a paper from a study funded by Gilead. Maybe her memory is a bit shonky
7. Favre, The Lancet Regional Health, July 2022
https://www.sciencedirect.com/science/article/pii/S2666776222001041#coi0001
The claim of this study was that, in a cohort of women followed up in pregnancy following COVID vaccination, there was a low incidence of miscarriage. The truth is that his study had no way of demonstrating miscarriage rates and the final rate shows that the study was either fraudulent or misrepresented.
There was no comparative cohort of unvaccinated patients and this was just a cohort of patients registered with a provider who then entered their information into a REDCAP registry (online database). In other words, if the provider didn’t want to register the miscarriage they didn’t need to.
Only 107 women received the vaccine in the first trimester and only 1 miscarriage resulted. On the basis of Viki Male’s claims of a 15%+ miscarriage rate this would be expected to give 16 miscarriages in a comparable cohort of that size. The probability of only one miscarriage occurring in a cohort of this size is less than one in a thousand. In fact, if you accept the higher miscarriage rate quoted in Zauche or any other paper related to Viki’s claims, the probability that there would only be one miscarriage in a cohort of 107 is less than 5%.
For a binomial calculation the probability of this cohort producing 1 miscarriage for a background rate of 12% is near zero, and for a background rate of 6% is 1:100. In other words, this data as presented is fake7.
The reason this study has ended up with this impossible data point is that it was being run by vaccine advocates who entered the data manually. In other words, if you had a miscarriage after you had the vaccine there was no way you were going to be entered in this study.
The paper is so amateur that the authors can’t even format their table. This is the PDF version. Great proof reading guys!
In the same table there are two subtables for “trimester of injection”. The first gives 7+12+25+28 = 72 patients injected in the first trimester out of a total of 894, and the second gives 1+1+2+3 = 14 patients injected in the first trimester out of a total of 901 patients.
Well, which is it? Neither of these, by the way, add up to 107 participants in the first trimester or to 840 total participants or to 1012 or any other number that was quoted in the methods. It’s also bizarre that the paper creates a cut-off for the first trimester at 14 weeks + 0 days, yet the tables refer to 12 weeks + 0 days.
This paper therefore should never have passed peer review and for that reason seems perfect for the now completely discredited Lancet (of Lancetgate fame).
The CONSORT diagram is an embarrassment. Pretty much over 50% of their cohort were excluded from each analysis for various frivolous reasons such as “of 399 women exposed before 20 weeks, 170 patients were excluded because they did not complete the questionnare”. It’s a mess.
It is also noteworthy that both the declarations of interest and the data itself are hidden from public view and can only be accessed on request.
In summary, absolute junk. This paper should never have passed peer review in a real journal, and therefore was perfect for the Lancet.
8. Trostle, AmJOG, 2021
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366042/
I’m not going to spend much time on this because it’s a small observational study from a previously unpublished obstetrician (presumably junior trainee) with no control group, despite the provision of a control group being totally feasible in an EMR study like this.
The gestation at the time of vaccination is not declared - simply that there were 8 of 124 women “vaccinated in the first trimester” who miscarried, giving a raw rate of 6.5%. The rate was actually higher than this as about a third of their cohort was vaccinated after 8 weeks. The cohort vaccinated before 8 weeks had a 9.5% raw miscarriage rate which is already higher than the expected rate after pregnancy confirmation as shown in Naert and could be either high or normal depending on the background demographics of the population. There is of course no reason that matched unvaccinated women could not have been include from the same EMR, so it is highly suspicious that they were not included.
It also uses a retrospective EMR record so is easily subject to the selection whim of the author, who is already biased by the Kharbanda study which I have addressed above. In fact this paper was so bad it immediately attracted negative commentary to which the author responded with a deflection to the Kharbanda paper.
“We agree that the spontaneous abortion rate is dependent on the population and that additional larger, multicenter studies with a control group of unvaccinated women would be better to evaluate this risk. The recent study by Kharbanda et al4 is better designed to assess the association between COVID-19 vaccination and spontaneous abortion. We recognize the limitations of our study in determining and reporting the risk of spontaneous abortion. We look forward to future studies that will provide further information on the impact of COVID-19 vaccination on pregnancy outcomes.”
Essentially, admitting this paper is worthless.
9. Brookstein-Peretz, Ultrasound Ob Gyn, 2021
https://obgyn.onlinelibrary.wiley.com/doi/full/10.1002/uog.23729
Viki has clearly misunderstood this paper as there were no women vaccinated in the first trimester and no miscarriages discussed. In fact “miscarriage” was only mentioned twice in the paper, once in table 5 which was dedicated to the 57 women who received the vaccine after 26 weeks and therefore could not miscarry, and the second time in the discussion.
Including this paper with the “miscarriage” tag suggests that Viki hasn’t even read it, and so I’m not even going to give it a “Viki pants-on-fire” rating. It’s not worth my time. However I’ll leave this here for people to make their own.
Conclusion (part 1)
Far from supporting Viki’s claims that
(1) there is no increase in miscarriage rates following the COVID “vaccines” and
(2) that the papers she has listed show that 100,000s of pregnancies demonstrate the safety of COVID “vaccines” it turns out that the papers she is quoting to support her claim are either totally junk, fabricated or misrepresented.
The take home from part 1 is this:
Despite the pomp and bravado, there is not ONE randomised controlled trial nor a prospective cohort study comparing vaccinated vs unvaccinated women in pregnancy.
This was absolutely by design.
There is no reason that this could not have been arranged, and if you find the studies in this article confusing - it's intentional.
Why? Because the pharma companies do not want those groups compared. In order to see what is really going on we will have to turn to the Calvert study.
In part 2.
EDIT: part 2 is now live
Debunking Viki Male: Part 2 - the Calvert Affair
Data Safety Monitoring Committee
https://archive.is/Nrk5e
I am happy to retract this statement or correct it on provision of the data and an explanation from the authors.
The infamous “Week 13” (and prior) of the UKHSA vaccine surveillance reports are no longer available in the COVID reports on the UKHSA’s website. Fortunately it is preserved here. See table 14, page 45 for the relative COVID infection rates between the vaccinated and unvaccinated per 100,000 population split by age. After this report, the UKHSA stopped compiling rates by vaccination status.
When used here, the word “fake” includes overtly fraudulent (made up) data as well as data that is cherry picked, or where data points are conveniently left out. Anything that misrepresents the true underlying data (“warts and all”) is “fake”
Automatic Like! Your diligence on the behalf of humanity is greatly appreciated. Please never forget that!
Reading off a computer screen is a miserable business, only got a third of the way. Going to have to print it and read it. But even just that far, for this failed musician who thinks too much I find I'm asking myself my stock question -- the one I've been asking myself for three years now -- over and over and over:
"How can I trust a medical doctor, or anything they tell me, ever again?"