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It was January 2020, the very beginning of COVID, when news articles began appearing that connected the genetics of the virus with gain-of-function research on bat coronaviruses at the Wuhan Institute of Virology.
These speculations were put to rest by an authoritative statement in the prestigious journal Nature Medicine, echoed by a summary in Science and an unusual affidavit in the Lancet signed by an impressive list of prominent scientists.
The message in the Nature Medicine article was dispositive: “Our analyses clearly show that SARS-CoV-2 is not a laboratory construct or a purposefully manipulated virus.”
But where was the support for this confident conclusion in the article itself?
The 2,200-word article in Nature Medicine (Anderson, et al) contained a lot of natural history and sociological speculation, but only one tepid argument against laboratory origin: that the virus’s spike protein was not a perfect fit to the human ACE-2 receptor.
The authors expressed confidence that any genetic engineers would certainly have computer-optimized the virus in this regard, and since the virus was not so optimized, it could not have come from a laboratory. That was the full content of their argument.
Most readers, even most scientists, take in the executive summary of an article and do not wade through the technical details. But for careful readers of the article, there was a stark disconnect between the Cliff Notes and the novel, between the article’s succinct (and specious) conclusion and its detailed scientific content.
This was the beginning of a new practice in the write-up of medical research. Recent revelations in the Fauci/Collins emails shed light on the origins of this tactic and the motives behind it.
In the past, if a company wanted, for example, to make a drug look more effective than it really was, it would choose a statistical technique that masked its downside, or it would tamper with the data.
What companies would not do, in the past, was describe the results of a statistical analysis that proves X is false, then publish it with an Abstract that claims X is true.
But this strange practice has become more common in the last two years. Academic papers are being published in which the abstract, the discussion section and even the title flatly contradict the content within.
Why is this happening? There are at least three possibilities:
- The authors cannot understand their own data.
- The authors are being impelled by the editorial staff to arrive at conclusions that match the ascendant narrative.
- The authors and editors realize the only way to get their results into publication is to avoid a censorship net that gets activated by any statement critical of vaccination efficacy or safety.
Before reaching any conclusions, let’s take a closer look at some examples of this troubling phenomenon arising in what should be the foundation of what is known: published scientific data.
In this article, we present five different published studies. Each to varying degrees exemplifies a disconnect between the data and the conclusions.
Example 1: ‘Phase I Study of High-Dose L-Methylfolate in updates Combination with Temozolomide and Bevacizumab in Recurrent IDH Wild-Type High-Grade Glioma’
This example is unrelated to the pandemic, but it typifies a common practice in the pharma-dominated world of medical research. If a remedy is cheap and out of patent, there is no one motivated to study its efficacy.
But research practice has gone well beyond neglect. In fact, investigators are skewing statistics to make cheap, effective treatments look ineffective if they are in competition with expensive pharma products.
This is ridiculously easy to do — all it requires is incompetence. Using the wrong statistical test, using a weak test when a stronger one applies — or just about any mistake in parsing the data — is far more likely to make compelling data appear random than the opposite.
Is it always incompetence? Or is it more often a well-thought-out deception that uses seemingly erudite analysis to lead the undiscerning reader into believing the wrong conclusion?
In the case of this article, a simple B vitamin (L-Methylfolate) was shown to double the life expectancy of 6 out of 14 brain cancer patients who received it, while showing no benefit (and no harm) to the other half of the patients.
The purple jagged line extending out to the right represents 40% of patients who lived dramatically longer when treated with L-Methylfolate (LMF).
The abstract reports that “LMF-treated patients had median overall survival of 9.5 months [95% confidence interval (CI), 9.1–35.4] comparable with bevacizumab historical control 8.6 months (95% CI, 6.8–10.8).”
The increase in median survival time is just a few months and not statistically significant. But the average survival time of the folate-treated group was more than double, and the difference was statistically significant (by my calculation, not in the article).
But the average is what is more commonly reported, and most readers don’t understand the difference between average and median.
The longest surviving patient on the B vitamin was still alive at the end of the study (3.5 years) when every one of the patients treated only with traditional chemo was dead before 1.5 years.
There were three different dosages in the study, (30, 60, 90 mg) and it was not reported whether the longest-living patients were receiving the highest dosages.
This is, in fact, a hugely promising pilot study about treating a common, fatal cancer with a simple vitamin. If it were an expensive chemotherapy drug instead of a cheap vitamin, you can be sure it would have been hailed as a breakthrough.
But this study will not create much excitement, and few oncologists will even know to prescribe methylfolate for their glioma patients.
Example 2: ‘Preliminary Findings of mRNA Covid-19 Vaccine Safety in Pregnant Persons’
Earlier this year, MacLeod et al used data from a prominent Centers for Disease Control and Prevention (CDC) study to calculate that for women in their first trimester, the rate of miscarriage following administration of an mRNA COVID vaccine was an alarming 82%.
On Jan. 7, the CDC released a report designed to dispel our misgivings about vaccinating pregnant women. Its conclusions were unequivocal:
“These data support the safety of COVID-19 vaccination during pregnancy. CDC recommends COVID-19 vaccination for women who are pregnant, recently pregnant, who are trying to become pregnant now, or who might become pregnant in the future.”
The Defender reported on the numerous flaws in this study. The most egregious deficiency was the dearth of pregnant women in the study who were vaccinated early in their pregnancy (less than 2%).
The authors admit their study could not quantify the risk of vaccine exposure in the first trimester: “First trimester vaccinations are not included in analyses stratified by trimester because few exposures occurred…”
How then can they recommend COVID vaccination for women who are “recently pregnant” if their analyses excluded women in their first trimester?
This report serves a purpose. People who read it superficially will find the reported results reassuring — including front-line doctors who don’t have time to evaluate the research critically.
The CDC chose to paint over troubling safety concerns with reassuring words that are unsupported by clear science.
Example 3: ‘Public Health Scotland COVID-19 & Winter Statistical Report’
There is a section of this report comparing vaccinated and unvaccinated rates of disease, preceded by a warning to the reader not to take the data at face value.
“PLEASE READ BEFORE REVIEWING THE FOLLOWING TABLES AND FIGURES There is a large risk of misinterpretation of the data presented in this section due to the complexities of vaccination data …”
The data the authors don’t want us to misinterpret say that people who have been vaccinated with one shot or three shots are 50% more likely to contract COVID-19 compared to people who are unvaccinated.
People who receive two shots are more than twice as likely to contract COVID-19. This is according to the authors’ own method of calculating age-standardized disease rates.
The authors emphasize it’s not about case numbers — it’s about severe outcomes, hospitalizations and deaths:
“Evidence suggests the COVID-19 vaccines are 90% effective at preventing a severe outcome of COVID-19. COVID-19 hospitalizations and deaths are strongly driven by older age, with most deaths occurring in those over 70 years old and having multiple other illnesses. But overall, you are less likely to be hospitalized if you are vaccinated with a booster.”
What data are they talking about? Here are results from their own data table:
The only substantial reduction is from people who received the third shot, which has only recently been available in Scotland. But for the three-shot cohort only, vaccination effectiveness is declining over the four weeks.
This adds to previous evidence that protection from the vaccine is short-lived, and each injection provides a shorter window of protection than the previous one. Also, note the hospitalization statistics may have been gamed.
Example 4: ‘Clinically Suspected Myocarditis Temporally Related to COVID-19 Vaccination in Adolescents and Young Adults’
Myocarditis, or inflammation of the heart, is a severe and life-shortening disease. It is virtually unknown in young people, but it is a recognized side effect of the COVID vaccines, especially in boys and young men.
This article summarizes the experience of 139 young patients (ages 12 to 20) who were hospitalized for myocarditis following vaccination.
19% of them were taken into intensive care.
Two required infusions of pressors and inotropes (potent intravenous drugs used to raise critically low blood pressure).
Every patient had an elevated Troponin I level. Troponin is an enzyme specific to cardiac myocytes. Levels above 0.4 ng/ml are strongly suggestive of heart damage. These young patients had a median Troponin I level of 8.12 ng/ml — over 20 times greater than the levels found in people suffering heart attacks.
“Conclusions: Most cases of suspected COVID-19 vaccine myocarditis occurring in persons <21 years have a mild clinical course with rapid resolution of symptoms.”
“Mild clinical course” — We suppose this refers to the 81% who did not go to the ICU or the fact that none died or required ECMO (Extracorporeal Membrane Oxygenation, a desperate means to keep the body oxygenated when a patient’s heart or lungs have completely failed).
In any case, every single person in this study was hospitalized. When does a “mild clinical course” require hospitalization for a two-day median length of stay?
“Rapid resolution of symptoms” — How would anyone know this? Myocarditis in older patients doubles the probability of death for the long term.
We don’t know what it will do to young boys in the long term, especially since every patient had some damage to their heart as evidenced by significantly abnormal troponin levels. And we don’t fully understand the mechanism by which the vaccines cause myocarditis.
Example 5: ‘Increases in COVID-19 are unrelated to levels of vaccination across 68 countries and 2947 counties in the United States’
This is the title of a paper by two statisticians from the Harvard School of Public Health, published on Sept. 30, 2021, in the European Journal of Epidemiology.
The title makes the important claim that there is no public health benefit from vaccination. COVID-19 is spreading at the same rate in different populations, unrelated to whether the population is mostly vaccinated or mostly unvaccinated.
It’s a powerful counterpoint to the ubiquitous demand that more people should undergo vaccination for the sake of their community.
The paper completely undermines the requirement of vaccination to attend meetings, concerts, theater and other public gatherings. It says there is no legitimacy to the creeping government vaccine mandates for travel.
But the data in the paper don’t show that vaccination and spread of COVID-19 are “unrelated.” In fact, there is a paradoxical relationship, an insidious relationship: The more vaccinated countries had more new COVID-19 cases (during the week when the survey was conducted). The correlation is significant (p=0.04).
Still, the authors conclude by explicitly recommending propagandizing of the unvaccinated: “In summary, even as efforts should be made to encourage populations to get vaccinated it should be done so with humility and respect.”
It may sometimes be wrong to promote flawed health policy, but apparently, it’s a good thing, so long as it is done with humility and respect.
Why would these researchers take the trouble to publish data that is so damning to the vaccine narrative, and then pull punches in the title and in the conclusions?
Are we to assume that these authors who have assiduously extracted data from 68 different countries and nearly 3,000 U.S. counties were unable to notice their meticulous scatter plot unequivocally demonstrates high vaccination uptake is associated with higher (NOT lower) prevalence of COVID-19?
This seems to be a different case from the first example, where shills for the pharmaceutical industry set out to create a deceptive narrative. We think it’s probable that in this case, soft-pedaling the implications of these glaring data may not have been the authors’ choice, but rather a decision by the journal’s editors.
We know from personal experience how difficult it is to get an article through peer review at most “reputable” medical journals when the results are out of sync with the COVID narrative.
It may well be that these authors fought hard to get their subversive message into print, and in order to get past peer review, they softened the language, especially, the title.
The church was once the most trusted institution in Europe. Then the bishops started selling indulgences — a kind of get-out-of-hell-free pass for rich sinners.
Today the most trusted institution is science.
This is true despite the fact that scientists are human, subject to error and to corruption.
Medical journals have become financially dependent on their advertisers, which are almost exclusively the pharmaceutical giants.
For several decades now, the “Church of Science” has been selling indulgences. With enough money, you could buy a scientific study that says what you want it to say.
Darell Huff’s book, “How to Lie with Statistics,” first published in 1954, remains the all-time best-seller in its field.
Recently, Gerald Posner documented the way in which the pharmaceutical industry has used its profits to affect science at every level, from medical researchers to journal editors to government regulatory agencies to the journalists who interpret science for the public.
Pressure is being placed on independent researchers by the journal editors and peer reviewers, many of whom have ties to Big Pharma. Valid studies, honestly reported, can be rejected for publication if they send a message that threatens corporate profits.
In the age of COVID, we see three reasons that an article’s conclusions might become detached from its statistical findings:
- Scientists have suddenly abandoned basic logic and reason. This is an implausible explanation because, as has been demonstrated above, these examples demonstrate diligence in gathering data. There is no reason why they would abandon diligence in arriving at reasonable conclusions.
- Shortcuts by pharmaceutical companies and their shills in academia. Rigging clinical trials the old-fashioned way is expensive and time-consuming. It’s also uncertain. Sometimes the truth rears its head even if a study is designed to conceal it. Even a study that is designed to fail might succeed when the inconvenient truths are sufficiently stubborn. How much easier it is to report the results and then tack on an abstract and a discussion section that say what you want to say, regardless of the data tables in the body of the article!
- Scientist authors are well aware of the pernicious censorship in scientific publication that has emerged in recent days. This is perhaps the most intriguing possibility. If researchers behind the study have some prestige and some influence, they still may find they have to soften their rhetoric in order to pass peer review. However, what we are witnessing today is more than a tendency to be “diplomatic” in their choice of words. What does it mean when their conclusions do not match the findings? Are they trying to tell us that they are gagged? Are they silently screaming at us to look at the data and not their interpretation of them?
The Nature Medicine article on the origins of the SARS-CoV-2 virus (reviewed first) seems to be an example of researcher corruption.
The article in the European Journal of Epidemiology (Example 5), which relates vaccination rates to COVID prevalence, is more likely an example of corruption by journal editors and peer reviewers.
In this instance, the data and conclusions are so disparate that it begs us to reconsider the cynical position that all scientists have been corrupted. Is there a better way for conscientious scientists to signal their community that they are being censored than by compiling solid data that tell a compelling story and then arriving at a nonsensical conclusion? Are they imploring us to read between the lines?
For the other four articles reviewed above, we leave it to your judgment — how do you think the conclusions came to be so disconnected from the statistical findings in these same articles?
Obviously, this blatant distortion of scientific write-ups is not a long-range strategy, but the world is moving fast, and people who count on their ability to shape scientific conclusions to their financial interests will be successful for long enough to do a great deal of mischief.
What will be the damage to the credibility of science when the dust clears?