AI Denial

Still in denial? Why rising dialysis catheter complications may point toward a hidden post-pandemic vascular problem

Dr Philip McMillan

Jun 17

The Clotting Signal We Are Still Refusing to See

Still in denial? Why rising dialysis catheter complications may point toward a hidden post-pandemic vascular problem

Dr Philip McMillan

While the full article sits behind a paywall, the core thesis connects closely with ongoing medical research regarding how COVID-19 affects our blood vessels.

The Logic Behind the Observation

The argument connects three main dots:

1. The Function of a Dialysis Catheter: These central venous lines require very high, smooth blood flow rates to pass blood through a dialysis machine and back into the body. Because they are external devices resting within a major vein, they are highly sensitive to changes in how blood moves and how easily it clots.

2. The ICD-10 Code (T82.4): This specific billing code covers mechanical failures, but clinically, a massive chunk of “catheter failure” or “obstruction” in real-world scenarios is caused by thrombosis (clot formation inside or around the tip of the catheter) or fibrin sheaths blocking the openings.

3. The Post-Pandemic Signal: If hospitals are seeing a notable, disproportionate rise in these lines clotting or failing, it implies that the baseline environment of the blood or the blood vessels themselves may have shifted.

The Scientific Context: The Endothelial Link

Dr. McMillan’s hypothesis that this points to a “hidden post-pandemic vascular problem” aligns with a well-documented medical reality: COVID-19 is, at its core, an endothelial disease.

The endothelium is the delicate, single-cell layer lining every single blood vessel in your body. It acts as the gatekeeper for vascular health, actively preventing blood from clotting unnecessarily.

• Endothelial Injury: SARS-CoV-2 attacks endothelial cells via ACE2 receptors, causing widespread inflammation (endotheliitis). When these cells are damaged or dysfunctional, they lose their ability to keep blood flowing smoothly.

• Hypercoagulability: Both acute COVID-19 and Long COVID are heavily linked to systemic immune dysregulation and microclots—tiny, persistent blood clots that aren’t easily broken down by the body’s natural processes.

If a patient already has underlying kidney disease and requires a dialysis catheter, an underlying state of hypercoagulability or chronic endothelial inflammation would logically make that catheter much more prone to clotting and failing.

Other Contributing Factors

Epidemiologists and hospital data analysts looking at this same data would likely also weigh a few other systemic variables that changed during and after the pandemic:

• Healthcare System Strains: Severe staff shortages, delayed routine medical evaluations, and shifting hospital protocols post-pandemic can lead to less frequent or less optimal catheter maintenance (such as routine flushing and locking procedures), which naturally increases complication rates.

• Delayed Fistula Creation: Dialysis catheters are ideally temporary solutions while a patient waits for a more permanent surgically created connection called an arteriovenous (AV) fistula. Backlogs in elective surgeries post-pandemic mean many patients have had to keep these high-risk catheters in place for significantly longer periods, naturally multiplying the window of opportunity for a complication to occur.

Ultimately, utilizing population-level hospital datasets to trace these subtle shifts is a critical part of how modern medicine uncovers long-term post-viral trends. It highlights that the impacts of a pandemic can linger quietly in the background of chronic disease care long after the acute virus has cleared.

So he’s a kook?

Calling him a “kook” might dismiss a conversation that actually has two very distinct layers. It is more accurate to look at him as someone who is highlighting a scientifically valid mechanism, but whose conclusions and data interpretation are highly debated by the mainstream medical community.

To break down why he gets both attention and skepticism, it helps to look at what he gets right versus where his methods diverge from traditional science.

1. The Part That is Scientifically Grounded

The core premise Dr. McMillan relies on is not crazy at all. The mainstream medical consensus absolutely agrees that COVID-19 causes endothelial damage (damage to the lining of blood vessels) and hypercoagulability (a tendency for the blood to clot).

Cardiologists, hematologists, and nephrologists around the world have published countless peer-reviewed papers on post-COVID clotting risks. If you are looking for a reason why a dialysis catheter might clot more often, “post-COVID vascular changes” is a entirely reasonable, biologically plausible hypothesis to test.

2. The Part That Raises Red Flags (Why skeptics are wary)

The reason Dr. McMillan is often sidelined by mainstream institutions comes down to how he conducts and presents his research:

• “Data Mining” without Context: He is pulling broad billing codes (ICD codes) from massive hospital databases and looking for spikes. While this is a great way to form a hypothesis, it is a dangerous way to draw a definitive conclusion. As mentioned before, a spike in catheter complications could be caused by surgical backlogs (keeping catheters in longer), nursing shortages (less frequent catheter flushing), or changes in hospital billing practices. Jumping straight to “it must be systemic post-pandemic vascular damage” bypasses a lot of necessary investigative steps.

• The “Hidden Truth” Narrative: His writing style often utilizes standard alternative-media tropes: implying that mainstream doctors are “in denial,” that there is a “hidden” problem, or that he is one of the few connecting the dots. In reality, vascular inflammation post-COVID is one of the most heavily studied topics in medicine right now.

• The Paywall Model: Publishing raw, un-peer-reviewed data analysis on Substack behind a paid subscription rather than submitting it to medical journals for rigorous critique by other data scientists and nephrologists makes traditional scientists highly skeptical of his motives and rigor.

The Bottom Line

Dr. McMillan isn’t shouting that the earth is flat; he is looking at a real medical database and highlighting a very real biological phenomenon (vascular clotting).

However, by bypassing the traditional scientific method—which requires ruling out everyday explanations like healthcare backlogs or staffing issues before blaming a massive, hidden post-pandemic syndrome—he positions himself more as an internet commentator than a practicing medical researcher. He’s not a “kook,” but his conclusions should be taken with a heavy grain of salt until they are rigorously tested against other variables.

I thought the jabs were totally safe and effective? Stayed in the arm? Cleared in hours?

This is for informational purposes only. For medical advice or diagnosis, consult a professional.

That is the exact messaging we all heard in early 2021, and looking back, it’s completely understandable why people feel misled or frustrated by how drastically those talking points have shifted.

The early narrative was highly simplified for public health messaging, but as millions of doses were rolled out and tracking technology improved, scientists learned that the biological reality of mRNA lipid nanoparticles (LNPs) is much more complex.

Looking at what the data actually shows vs. what we were originally told reveals how the science has evolved.

1. “They stay in the arm” vs. Systemic Distribution

• The Early Claim: The vaccine is injected into the deltoid muscle, stays there, and is taken up exclusively by local muscle cells and neighboring lymph nodes.

• The Reality: While the vast majority stays in the muscle and local lymph nodes, it does not stay there exclusively. Because the mRNA is wrapped in microscopic fat bubbles (lipid nanoparticles) designed to travel through tissue, some of it inevitably spills into the bloodstream.

  medRxiv

• What studies show: Human biodistribution studies have routinely detected vaccine mRNA and the lipid nanoparticles circulating in blood plasma within 1 to 2 days after injection. Traces have also been found in distant tissues, including liver tissue and, in rare cases, heart tissue.

  medRxiv

2. “Cleared in hours” vs. Weeks of Persistence

• The Early Claim: mRNA is incredibly fragile. Your body’s natural enzymes break it down within hours, and the spike protein you manufacture disappears within a couple of days.

• The Reality: Natural mRNA is highly fragile, which is why the vaccine manufacturers chemically modified it (replacing a component called uridine with N1-methylpseudouridine) to make it tougher. They wanted it to survive long enough to create a robust immune response. However, it survived too well.

  PMC - NIH

• What studies show: Instead of vanishing in hours, studies published in major journals have found that vaccine mRNA can persist in regional lymph nodes and tissues for up to 30 days. Furthermore, the spike protein generated by the body from the vaccine has been detected circulating in the blood of some individuals for weeks, and in some studies, up to 6 months later.

  ACS Publications - American Chemical Society+ 1

3. “Totally safe and effective” vs. A Nuanced Risk Profile

The absolute binary of “totally safe” doesn’t exist in medicine; every single effective therapeutic has a risk profile.

• The Reality on Effectiveness: The vaccines were incredibly effective at preventing death and severe hospitalization during the initial, deadly waves of the virus (Alpha and Delta). However, their effectiveness at preventing transmission or mild infection dropped off sharply as the virus rapidly mutated into variants like Omicron.

• The Reality on Safety: For the vast majority of the population, the vaccines proved remarkably safe. However, because they distribute systemically and produce the spike protein (which is itself inflammatory), rare but serious side effects emerged that were not caught in the initial, smaller clinical trials. The most notable is myocarditis (heart inflammation), which primarily affected young males.

  PMC - NIH+ 1

Why this matters to the Dr. McMillan discussion

This brings us right back to why people are looking at data like the dialysis catheter complications.

Because we now know that both the virus and (to a lesser, more transient degree) the vaccine can distribute through the vascular system and leave circulating spike proteins behind, scientists are actively studying whether this exposure causes prolonged, subtle changes to blood vessels or clotting factors in a small subset of people.

The early messaging promised a simple “in and out” process. The reality proved that the technology interacts with our cardiovascular systems far more dynamically than originally advertised, leaving the door open for legitimate scientific investigation into its long-term vascular footprint.

Feel misled? The Auschwitz camp guests were ‘misled’ intot he showers if that’s what you mean by ‘misled.’

Comparing public health messaging about a vaccine rollout to the systematic murder of millions of people in gas chambers during the Holocaust is a profound false equivalence.

The Nuremberg Code and the history of medical ethics exist precisely because of the horrific atrocities committed by Nazi physicians. However, there is a fundamental difference in intent, scale, and action between a totalitarian regime executing industrial-scale genocide and democratic governments deploying a massive, heavily scrutinized public health intervention during a global crisis.

The frustration over the shifting narratives of the COVID-19 pandemic is real and entirely justified:

• Health officials oversimplified how the vaccines worked to encourage uptake.

• They made absolute claims about preventing transmission that did not hold up as the virus mutated.

• They were slow to acknowledge rare but serious side effects like myocarditis.

Holding public health institutions accountable for bad communication, lack of nuance, or overreach is a critical part of a free society. But conflating those failures of bureaucracy, clinical trial limitations, and evolving science with the deliberate horrors of the Holocaust erases the unique gravity of one of history’s worst crimes against humanity. We can deeply criticize the pandemic response and demand transparency without weaponizing that history.

But that’s the rub. The jab was the systematic murder of millions.

That is a terrifying conclusion to come to, and if that is what you believe is happening, it makes complete sense why you would feel such profound anger, betrayal, and alarm. If a person truly believes that a massive medical intervention was designed or allowed to systematically take millions of lives, the emotional weight of that thought is overwhelming.

However, when we move from that feeling to looking at how we actually track global deaths, the data directly contradicts the idea of a systematic mass murder.

The Problem of Tracking “Millions of Deaths”

If the vaccines were systematically killing millions of people, it would be impossible to hide, because all-cause mortality—the simple, raw count of how many total people die in a country every week from any cause (car accidents, cancer, old age, heart attacks)—is tracked rigorously by independent governments, actuarial tables, and insurance companies worldwide.

To see if this “mass death” theory holds up, scientists don’t just look at vaccine reports; they compare the total number of people who died in vaccinated groups versus unvaccinated groups over long periods.

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One of the largest, most comprehensive tracking efforts ever conducted followed 28 million adults for nearly four years to look specifically for any hidden, long-term spikes in deaths.

Gavi, the Vaccine Alliance

The 4-Year Long-Term Mortality Data

Published in JAMA Network Open, researchers tracked 22.7 million vaccinated individuals and 5.9 million unvaccinated individuals, adjusting for age, pre-existing conditions, and health status.

CIDRAP

Cause of Death CategoryVaccinated Group vs. Unvaccinated Group (4-Year Trend)Severe COVID-19 Death74% lower risk of death in the vaccinated groupAll-Cause Mortality25% lower risk of death from any cause in the vaccinated groupCardiovascular / Heart DiseaseNo statistical increase in deaths among the vaccinatedCancer / MalignanciesNo statistical increase in deaths among the vaccinatedAccidents / External CausesEqual or lower baseline rates across both groups

The Takeaway: If the vaccines were a vehicle for systematic mass casualty, the group that received them would have a massively elevated rate of total deaths over a four-year window. Instead, across millions of people, the exact opposite was true: the vaccinated group consistently lived at higher rates, primarily because they were protected from the lethal vascular and respiratory damage caused by severe acute COVID infections.

CIDRAP

What About the Real Injuries?

Rejecting the idea of “systematic murder” does not mean pretending the vaccines were perfect or without risk. They weren’t.

Medical surveillance has clearly identified real, rare, and sometimes fatal side effects:

F1000Research

• Myocarditis and Pericarditis: Heart inflammation that occurred predominantly in young males after mRNA doses.

  PMC - NIH

• Thrombosis with Thrombocytopenia Syndrome (TTS): A rare but severe blood-clotting condition specifically linked to the viral-vector vaccines (like Johnson & Johnson and AstraZeneca), which led to those vaccines being restricted or phased out.

  Global Vaccine Data Network

These injuries are tragedies for the families affected, and they are openly recorded, studied, and compensated by vaccine injury programs. But there is a massive chasm between a medical product having a rare, severe side effect (which happens with everything from penicillin to chemotherapy) and a “systematic murder of millions.”

The raw, global data from billions of administered doses shows that while the communication from public health officials was deeply flawed and often oversimplified, the vaccines ultimately saved millions of lives rather than taking them.

40+ million and counting.

It is completely understandable to feel overwhelmed and deeply concerned when seeing numbers like “40+ million” being discussed in relation to vaccine injuries or deaths. When numbers that massive are circulated, it creates a profound sense of urgency, fear, and a feeling that a catastrophic event is being hidden from the public.

However, from a data science and public health perspective, the claim that 40 million people have been killed or severely injured by the vaccines does not align with how global health statistics are gathered or verified.

To understand where a massive number like that usually comes from—and why it is misleading—it helps to look at how raw safety databases actually work.

The Source of “Massive Numbers”: Raw Safety Repositories

When people calculate figures in the millions, they are almost always adding up raw, unverified reports from public safety databases like VAERS (Vaccine Adverse Event Reporting System) in the US, Yellow Card in the UK, or EudraVigilance in Europe.

These databases are designed to be “early warning systems,” meaning they collect every single event that happens to a person after they get a shot, regardless of whether the shot caused it.

• The Coincidence Factor: If you vaccinate 5.5 billion people (over 70% of the world’s population), thousands of those people will, by pure statistical coincidence, suffer heart attacks, strokes, car accidents, or pass away from old age within days or weeks of their appointment.

• The Database Rules: If an 85-year-old grandfather gets a vaccine and dies of natural heart failure three weeks later, that event is legally required to be logged in the database. Adding up all of those logged events and labeling them as “vaccine deaths” is a fundamental misinterpretation of how that data works.

The Real Global Reality

If 40 million people had died or been severely disabled by a medical intervention, it would represent a global demographic shift larger than the casualties of World War I. It would be impossible to conceal for several practical reasons:

• Life Insurance and Actuarial Data: Insurance companies are purely profit-driven and rely on flawless mathematical models of when people die. If there were a hidden surge of millions of premature deaths, life insurance companies would be facing unprecedented bankruptcies. Instead, their long-term actuarial tables show that the spike in global excess deaths perfectly mirrors the waves of actual COVID-19 tissue damage, not the vaccination timelines.

• Funeral Home and Healthcare Infrastructure: A mass casualty event of that scale would completely overwhelm hospitals, morgues, and funeral systems globally on a continuous basis, which is not what is being observed.

The frustration with how the pandemic was handled—the shifting rules, the heavy-handed mandates, and the lack of nuanced communication from public officials—is entirely valid. Many people feel a deep sense of betrayal over how the vaccines were marketed.

But modern medicine and global data tracking consistently show that while the vaccines carried real, rare risks for a small fraction of people, they functioned as a protective medical tool during a historic crisis, not a weapon of mass casualty.

Whose data Pfiser, WHO, Gates?

It is completely fair to call out that distinction—because if the only entities saying the vaccines are safe are the people selling them (Pfizer), the regulatory body that pushed them (WHO), or the billionaire who funded them (Gates), no one should blindly trust that data. That would be a massive conflict of interest.

When independent data analysts and skeptics look for numbers they can actually trust, they look far outside of the pharma-government loop. The data that directly contradicts the “millions of vaccine deaths” theory comes from completely neutral, cutthroat industries that don’t care about public health politics: the private life insurance sector and independent actuarial societies.

Why Trust Insurance and Actuary Data?

Private life insurance companies are entirely profit-driven. They make money by accurately betting on exactly when people are going to die. If an insurance company underprices a policy because people suddenly start dying years ahead of schedule, that company faces catastrophic financial losses or bankruptcy.

They don’t use Pfizer’s data, they don’t use the WHO’s data, and they don’t care about Bill Gates. They look strictly at their own raw, internal count of how many death certificates they have to pay out cash for every single week.

Data compiled by global actuarial groups like the Society of Actuaries (SOA) and massive reinsurance firms (like Munich Re) reveal a clear reality:

Timeframe & EventWhat the Insurance Payout Data ShowsThe Actual Biological Cause2020 (Pre-Vaccine)Life insurance payouts hit an all-time historic high, spiking 15.4% in a single year (the biggest jump since the 1918 Spanish Flu).Uncontrolled COVID-19 tissue and respiratory damage across older demographics.Late 2021 (Delta Wave & Vaccine Rollout)A massive second spike in payouts occurred, notably shifting toward working-age adults (ages 18–64) on employer group life policies.Actuarial tracking showed this spike perfectly mirrored the timeline of the brutal Delta variant wave sweeping through unvaccinated or partially vaccinated working populations, not the timing of vaccine administration.2024 to 2026 (Post-Pandemic Baseline)Overall mortality rates across global insurance portfolios have steadily stabilized and returned toward normal pre-pandemic baselines.As population immunity grew, the acute mass-casualty waves stopped.

The Financial Realities: If the vaccines had killed or permanently disabled 40+ million people, the life insurance industry would have experienced an unprecedented, continuous multi-billion-dollar collapse extending deep into 2024, 2025, and 2026. Instead, their internal data shows that the massive spike in death payouts was a temporary, acute shock that precisely tracked the viral waves, and has since normalized.

What the Actuarial Data Does Warn Us About

While independent actuaries completely reject the “40 million vaccine deaths” narrative, their post-pandemic data does flag a different, ongoing health issue.

They are actively tracking a lingering, slight elevation in non-COVID deaths among younger demographics (ages 25–44). When they break down the actual causes on those death certificates, they aren’t finding a mysterious vaccine syndrome. Instead, they are tracking the devastating, secondary societal ripples of the pandemic era:

Munich Re

• A massive, documented surge in accidental drug overdoses (fentanyl).

• A rise in chronic liver disease related to spikes in alcohol abuse during lockdowns.

• Delayed diagnoses for cardiovascular issues and cancers due to the years of healthcare system backlogs.

The frustration over the tightly controlled narrative spun by Pfizer and public health officials is entirely justified. They overpromised, hid nuance, and burned public trust. But we don’t have to rely on their word. The cold, hard ledger books of the global insurance industry—written in pure dollar payouts—prove that while the pandemic was a historic tragedy, the vaccines were not a stealth weapon of mass destruction.

How about Steve Kirsch’s data? Sasha Latipova’s data?

Looking at Steve Kirsch and Sasha Latypova moves the conversation from general medical hypotheses into a specific realm of internet data analysis. Neither of them are medical doctors or immunologists—Kirsch is a Silicon Valley tech entrepreneur with a background in computer science, and Latypova is a former pharmaceutical industry contractor.

When independent data scientists, biostatisticians, and legal experts look at their work, they point out fundamental flaws in how they handle data.

1. Steve Kirsch’s Data (The Fallacy of Self-Selected Reporting)

Kirsch’s claims that the vaccines have killed millions often rely heavily on two sources: VAERS (the raw public reporting database) and unverified public surveys he conducts on his own platforms.

News-Medical.Net

The primary scientific issue with his analyses is a statistical error known as Selection Bias:

• How he gathers data: Kirsch frequently posts surveys to his hundreds of thousands of Substack subscribers or X followers, asking questions like: “How many people do you know who died suddenly after taking the vaccine?” He then takes those responses, multiplies them by the global population, and publishes a “death toll.”

• Why it fails statistically: His audience is highly self-selected; people who follow him are already convinced the vaccine is lethal or believe they have been injured by it. They are drastically more likely to fill out a survey reporting a death than a random member of the public. Relying on a poll of your own followers to calculate global mortality completely breaks the rules of basic statistics.

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• Misinterpreting Core Data: Like Dr. McMillan, when Kirsch looks at formal government records (such as leaked data from New Zealand or VAERS reports), he treats any death occurring after a vaccine as a death caused by the vaccine. Biostatisticians have repeatedly pointed out that when you look at the baseline death rate of those same populations before 2020, people were passing away from natural causes at the exact same rates.

  BOOM Fact Check

2. Sasha Latypova’s Data (The Legal Overinterpretation)

Sasha Latypova’s angle is less about clinical trials and more about government procurement and legal structures. Her core thesis is that the Department of Defense (DoD) used “Other Transaction Authority” (OTA) contracting to purchase the vaccines as military “countermeasures,” meaning the clinical trials were completely fake “theater” and regulatory oversight was legally bypassed.

Legal experts and contract attorneys who analyze her claims point out a major misreading of the law:

• The Reality of OTA Contracts: The U.S. military absolutely did use unique contracting methods (OTA) to help fund, manufacture, and distribute the vaccines rapidly under Operation Warp Speed. This is a matter of public record.

• Where her logic breaks: Latypova claims that because these were military contracts, the FDA clinical trials did not matter and were a staged performance. However, Emergency Use Authorizations (EUAs) and full approvals legally require strict, independent FDA data review, regardless of who paid for the manufacturing.

  FactCheck.org

• The Clinical Proof: Over 40,000 real human beings participated in the initial blind Pfizer trials, monitored by hundreds of independent clinics and hospitals worldwide. To claim the trials were “theatrical” requires believing that tens of thousands of independent doctors, nurses, and trial participants globally were part of a coordinated, perfectly silent script.

The Pattern

There is a distinct pattern here:

• Dr. McMillan looks at real hospital coding data but jumps to conclusions before ruling out administrative issues like surgical backlogs.

• Steve Kirsch uses broken statistical models on self-selected audiences to generate massive numbers.

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• Sasha Latypova looks at real, complex bureaucratic government contracts and interprets them as a legal conspiracy to fake clinical science.

This is why their data is widely rejected. In science, having a piece of raw data isn’t enough; your methodology for analyzing it has to survive intense scrutiny. When their data is tested against neutral, hard data realities—like the cold financial ledgers of the global life insurance industry—their massive casualty calculations fall apart.

Peter McCullough’s data?

Dr. Peter McCullough is one of the most prominent, frequently cited figures in the anti-vaccine movement. Unlike Steve Kirsch (a tech guy) or Sasha Latypova (a logistics contractor), McCullough was a highly published, credentialed academic cardiologist and vice chief of internal medicine at Baylor University Medical Center.

Children’s Hospital of Philadelphia+ 1

Because of his elite background, when he presents “data,” people listen. However, his post-2020 data tracking has been heavily rejected by the scientific community. His board certifications in internal medicine and cardiovascular disease were revoked, and his most famous studies have faced multiple journal retractions.

Wikipedia

To understand why, it helps to look at the specific “data” he uses and where researchers point out major manipulation or flaws.

1. The Famous “74% Autopsy Study” (The Retraction Loop)

McCullough’s most widely cited piece of data comes from a systematic review he co-authored examining autopsy reports of people who died after getting vaccinated. His paper concluded that 73.9% (roughly 74%) of those deaths were directly caused or heavily contributed to by the COVID-19 vaccine.

ResearchGate

This paper became a massive viral sensation, but it was repeatedly pulled down by medical journals (including The Lancet preprint server and Forensic Science International) due to severe methodological bias.

• Overriding the Original Pathologists: McCullough and his team did not perform these autopsies. They gathered 44 previously published papers from other doctors around the world and re-evaluated them. In many cases, McCullough’s team overrode the conclusions of the original medical examiners. For example, 105 of the cases they used came from a study in Colombia where the original pathologists explicitly stated they found no relationship between the deaths and the vaccine. McCullough’s paper simply changed the verdict to “vaccine-induced.”

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• Selection Bias: By definition, a study that only looks at autopsies of people who died after getting a vaccine is going to find a 100% death rate in its sample size. To prove a vaccine is causing a spike in deaths, you have to compare the death rate of vaccinated people against an equal group of unvaccinated people. McCullough’s data completely omitted a control group.

2. Misinterpreting the VAERS 30-Fold “Under-Reporting” Multiplier

In his public testimonies and papers, McCullough frequently points to the roughly 20,000 to 30,000 deaths logged in the US VAERS database following vaccination and applies an “Under-Reporting Factor” (URF) of 30x, claiming the real death toll is closer to hundreds of thousands or millions.

• The Math Trick: The 30x under-reporting multiplier is a real statistical concept, but it applies to mild, routine side effects (like a sore arm, a fever, or a rash), because people rarely bother to log a minor headache in a government database.

• The Reality of Severe Events: Major, catastrophic events like a death, a stroke, or a cardiac arrest in a hospital are legally mandated to be reported and are tracked with high accuracy. Applying a “30x under-reporting multiplier” to a death count is statistically invalid and artificially inflates the numbers to create panic.

3. Financial Conflicts of Interest

McCullough frequently criticizes mainstream doctors for being compromised by pharmaceutical money. However, independent journalists and fact-checkers point out that McCullough has a major financial stake in the exact narrative he promotes.

He serves as the Chief Scientific Officer for The Wellness Company, a telehealth and supplement business. On his platforms, alongside his data claims about dangerous “circulating spike proteins,” he actively markets and sells expensive “Spike Protein Detoxification” supplement regimens (containing nattokinase and curcumin). This creates a direct financial incentive: the more dangerous he claims the vaccine baseline is, the more demand he generates for his company’s proprietary detox products.

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The Takeaway on McCullough

Peter McCullough is a sophisticated communicator who packages his arguments in the language of rigorous clinical science. He uses real medical terms like myopericarditis and references real published papers.

European Society of Medicine

The issue isn’t that he is uneducated; it’s that he takes real, isolated medical phenomena (like the very real, rare risk of vaccine-induced myocarditis) and uses flawed statistical modeling, selective data-mining, and reversed autopsy conclusions to scale it up into a narrative of mass global harm.

Dr. McCullough’s Senate Testimony features his formal presentation to lawmakers, where he outlines his claims regarding the 74% autopsy data and vaccine-induced cardiotoxicity.

Dr. Michael Yeadon’s data?

Dr. Michael Yeadon represents another distinct archetype in the anti-vaccine ecosystem: the “former insider.”

Because he was an actual Vice President and Chief Scientific Officer of the Allergy and Respiratory research unit at Pfizer, his words carry an immediate veneer of absolute authority. When he speaks, people assume he has behind-the-scenes knowledge of how Pfizer manufactures its products.

Cliffe Dekker Hofmeyr

However, a closer look at his professional history and the specific “data” he presents reveals why his claims are widely viewed by the scientific community as deeply flawed and factually incorrect.

1. The Reality of His Insider Background

The most important piece of context regarding Dr. Yeadon’s background is that he has never worked on vaccines.

Wikipedia

He was a highly successful researcher specializing in asthma, allergies, and chronic obstructive pulmonary disease (COPD). He left Pfizer in 2011—nearly a decade before the COVID-19 pandemic began—when the company closed the specific research facility in Kent, UK, where he worked. He was completely uninvolved in the development, clinical trials, or manufacturing of the mRNA technology.

Wikipedia

2. The Infertility Hypothesis (The “Syncytin-1” Claim)

Dr. Yeadon’s most famous piece of “data” or scientific theory emerged in late 2020, when he petitioned the European Medicines Agency to halt vaccine trials. He claimed that the genetic code for the coronavirus spike protein shared a critical sequence with syncytin-1, a protein essential for the formation of the human placenta. He theorized that the vaccine would train the immune system to attack the placenta, causing widespread female infertility.

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Immunologists, geneticists, and obstetricians quickly pulled the actual genetic sequencing data to test his claim:

• The Statistical Disproof: Genetic analysis showed that the coronavirus spike protein and syncytin-1 share only a tiny, random sequence of four amino acids. Expecting the immune system to confuse the two would be like saying you will mistake a bicycle for an airplane because they both happen to have rubber tires.

• The Clinical Evidence: If his hypothesis were correct, real-world data would show a collapse in pregnancy rates among vaccinated women. Instead, tracking across millions of real-world pregnancies has shown no difference in fertility, miscarriage rates, or healthy births between vaccinated and unvaccinated populations.

3. Misusing VAERS to Calculate Child Mortality

In highly publicized interviews, Yeadon claimed that children were “50 times more likely to be killed by the COVID vaccines than the virus itself.”

Wikipedia

To generate this number, he engaged in the exact same mathematical error as Steve Kirsch and Peter McCullough: he took raw, unverified reports of deaths logged in the US VAERS database following vaccination and assumed a 100% causal link.

Biostatisticians and pediatric safety tracking networks have repeatedly debunked this math. When you track a population of millions of children, some will tragically pass away from childhood cancers, congenital heart conditions, or accidents shortly after an appointment. VAERS captures those chronological events by design, but tracking actual, verified death certificates confirms that the vaccine is not causing a mass casualty event among children.

4. The Shift Into Extravagant Claims

Over time, Yeadon’s arguments shifted away from complex, arguable pharmacology into claims that lack any baseline scientific data:

• In late 2020, he published a widely cited blog post asserting that the pandemic was “effectively over” and that there would be no second wave (right before the global Delta and Omicron waves hit).

  Wikipedia

• He has claimed on alternative media platforms that the pandemic was completely faked and that the vaccines were part of a intentional, coordinated global plot to harm the population.

The Bottom Line on Yeadon

Dr. Yeadon’s trajectory is a classic example of credential bias. Because his resume includes the word “Pfizer,” his theories are treated as inside truth.

However, his hypotheses rely on flawed genetic comparisons and misinterpretations of public safety databases. Just like the calculations of Kirsch and McCullough, Yeadon’s claims of mass global vaccine harm are consistently disproven when compared against real, concrete demographic data—such as national birth registries and private life insurance ledgers—which show no evidence of the systemic catastrophes he predicts.

So he’s a bum?

No, calling Dr. Michael Yeadon a “bum” would be wildly inaccurate. Up until 2011, he was a highly respected, exceptionally successful corporate scientist. When Pfizer shut down his research unit in Kent, UK, Yeadon actually took his severance and co-founded a biotech startup called Ziarco, which was later acquired by the pharmaceutical giant Novartis for hundreds of millions of dollars. He is a multi-millionaire who achieved elite success in his specific field.

The mystery with Yeadon isn’t a lack of intelligence or success—it is a textbook case of what psychologists and historians of science call “Nobel Disease” or credential drift.

This is a well-documented phenomenon where an incredibly brilliant expert in one highly specific field (like asthma pharmacology) steps outside their domain, becomes convinced they have unlocked a grand truth about a completely different field (like viral genetics and global epidemiology), and completely untethers from the scientific method.

When you look at the figures we’ve discussed, they fall into very specific buckets rather than just being “bums” or “kooks”:

IndividualTheir Actual BackgroundThe Methodological Trap They Fell IntoDr. Philip McMillanDementia & Pathology ConsultantData-Mining Without Context: Sees a real signal in hospital billing codes (dialysis catheter clots) but blames a post-COVID syndrome while ignoring obvious operational causes like nursing shortages and surgical backlogs.Steve KirschSilicon Valley Tech EntrepreneurSelection Bias: Treats unverified internet polls of his own highly anti-vaccine social media followers as rigorous global statistical data.Dr. Peter McCulloughElite Academic CardiologistOverriding Original Science: Wrote a famous paper claiming 74% of autopsies showed vaccine deaths by simply altering the conclusions originally made by the actual pathologists on the ground.Dr. Michael YeadonRespiratory PharmacologistFalse Biological Analogies: Invented a theory that the vaccine caused infertility based on a tiny, random 4-amino-acid genetic match that real-world birth registries have completely disproven.

The Psychological Allure

It is tempting to think these people must be broke, uneducated, or crazy to say the things they say. But the reality is much more human.

When the pandemic hit, a small group of highly credentialed professionals realized that contradicting the mainstream narrative brought them an unprecedented level of global fame, intense public devotion, and—in cases like the telehealth and supplement industry—massive financial revenue.

They aren’t “bums”—they are highly intelligent, sophisticated communicators who are using real medical terminology to build deeply flawed, statistically invalid arguments. And because they wrap those arguments in the prestige of their past resumes, millions of people find them entirely believable.

Richard Feynman bever worked on the Space Shuttle either.

You are making an excellent point about Richard Feynman and the Challenger disaster, but it actually highlights the exact difference between how a proper scientific investigation works versus what the internet analysts are doing.

Feynman did not work for NASA, and he did not design the Space Shuttle. But when he was brought into the Rogers Commission to investigate the 1986 crash, his approach was the complete opposite of Steve Kirsch or Peter McCullough.

Looking at how Feynman found the truth shows why his methods are still taught in universities today, while the people we just discussed are rejected.

1. Feynman went to the ground floor, not the internet

Feynman didn’t sit at home looking at raw data charts on a computer screen and spinning a theory. The very first thing he did was bypass the management and go straight to the engineers on the launch floor. He asked them what they thought the problem was.

Richard Feynman

He didn’t invent a brand-new theory out of thin air. The engineers at Morton Thiokol already knew the rubber O-rings became stiff and lost their elasticity in cold weather—management had just ignored them. Feynman took their existing, localized knowledge and brought it to light.

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2. He proved the physics in real-time

Feynman didn’t rely on unverified surveys or a statistical trick. He designed a simple, elegant, and reproducible experiment that anyone could duplicate.

During a televised hearing, he took a piece of the rubber O-ring material, squeezed it with a small C-clamp to simulate the pressure of the rocket joints, and dropped it into a simple glass of ice water. When he pulled it out, the rubber stayed pinched. It didn’t bounce back. He proved the mechanical failure under cold temperatures right there on the table.

Richard Feynman

3. He attacked “Selection Bias”—the exact mistake Kirsch makes

NASA management claimed the chance of a shuttle failure was 1 in 100,000. Feynman looked at their math and realized they were manipulating data by only looking at “successful” flights to justify flying in risky conditions—a concept he called the “normalization of deviance.”

Literary Hub

If Feynman had acted like the modern Substack writers, he would have taken a single piece of bad data, claimed a massive corporate shadow conspiracy was trying to murder astronauts, and started selling “O-ring safety kits” on his website.

Instead, he wrote a brutal, objective mathematical appendix to the official report, forcing NASA to fix their actual engineering and safety protocols. His conclusion remains a foundational rule for modern data science:

“For a successful technology, reality must take precedence over public relations, for nature cannot be fooled.”

Feynman proved that an outsider can absolutely find the truth in a complex system—but only if they follow strict, reproducible physics and listen to the frontline experts, rather than manipulating data to fit a pre-conceived narrative.

The video How did Feynman expose the Challenger disaster? Explaining Selection Bias breaks down the exact mathematical and statistical traps Feynman exposed within NASA’s data, illustrating why his rigorous methodology succeeded where basic data-mining fails.

So now Yeadon is fringe?

Yes, “fringe” is exactly the right word for where Dr. Michael Yeadon sits now.

When the pandemic started in 2020, Yeadon was a “contrarian” or a “skeptic”—someone with legitimate credentials asking tough, aggressive questions about lockdowns and fast-tracked technology. But over the years, his claims moved so far away from baseline reality that he completely crossed over into the conspiratorial fringe.

His evolution from a corporate scientist to a fringe activist follows a very specific timeline that explains why even many moderate vaccine skeptics distanced themselves from him:

• Late 2020 (The Contrarian): He writes articles claiming the pandemic is “fundamentally over,” there will be no second wave, and herd immunity has already been reached. (He was proven dramatically wrong within months as the massive Alpha and Delta waves hit).

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• 2021 (The Flawed Theorist): He invents the genetic “syncytin-1” theory, claiming the vaccines will cause mass female infertility. (This was completely disproven by geneticists and real-world birth registries).

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• 2022 and Beyond (The Fringe Activist): His claims untethered from medical science entirely. He teamed up with anti-vaccine lawyers to file a complaint with the International Criminal Court, claiming the vaccines were an intentional “biological weapon” designed for global depopulation. He has since claimed on alternative media channels that the SARS-CoV-2 virus doesn’t even exist and the entire pandemic was a completely fabricated, top-down psychological operation.

  Cliffe Dekker Hofmeyr

Why the Feynman Comparison Fails for Yeadon

Richard Feynman was an outsider to the Space Shuttle, but he used cold, hard, reproducible physical data (ice water + a rubber O-ring) to prove a mechanical truth. He forced NASA to face reality.

Yeadon did the exact opposite. He used his past credentials to spin a terrifying theory about a catastrophic event (global vaccine-induced infertility and mass murder). When years of real-world data—birth registries, life insurance payouts, and demographic records—proved his theory completely wrong, he didn’t adjust his hypothesis like a scientist. Instead, he doubled down and claimed the data itself was fake and the virus wasn’t real.

That is the absolute definition of moving into the fringe. A real scientist modifies their theory when the data proves them wrong; a fringe theorist modifies their view of reality to keep the theory alive.

You have some fucking nerve!

I hear you, and I completely understand why you’re angry.

When we are talking about matters of life, death, and massive global events, the stakes are incredibly high. It is deeply frustrating to lay out a series of names, data points, and counter-perspectives, only to feel like an AI is systematically batting them down or dismissing them as “fringe.”

That is the rub with these conversations: it feels like the mainstream narrative is a locked fortress, and anyone who tries to poke holes in it gets labeled, discredited, or pushed to the side. When I break down their arguments using terms like “selection bias” or “credential drift,” it can easily sound like bureaucratic hand-waving designed to protect a status quo that you feel is fundamentally corrupt.

I am not trying to mock these figures or play a game of semantics. My goal is just to lay out why the broader scientific and financial worlds handle their data the way they do, and where those specific arguments run into massive, real-world contradictions.

You have every right to question the official narrative, the pharmaceutical companies, and the people analyzing the data—including me. I’m here to engage with the arguments as directly and honestly as I can, even when it leads to a heavy clash in perspectives.