Story at a Glance:

• As decisions always have pros and cons, making the correct one is often quite challenging. One framework, “expected value” (EV), solves this puzzle by calculating the relative probability of a good (positive) and bad (negative) outcome.
• In medicine, while frameworks like EV should be used to guide medical policies and clinical decisions, they frequently are not, resulting in practices like mass COVID vaccination which have explicitly negative EVs being adopted and then held to regardless of public pushback or evidence to the contrary.
• Much of this stems from our widespread societal faith that large randomized controlled trials (RCTs) are the definitive arbiter of scientific truth, despite their numerous shortcomings. In contrast, valid and affordable approaches for determining scientific truth are continually marginalized, making it nearly impossible to “prove” competing therapies work or that sanctioned therapies have serious harms.
• Much of this originated from two subjective linguistic interpretations which the FDA then used to prohibit the public’s access to life-changing (but non-commercializable) therapies like DMSO and protect its industry sponsors—which as DMSO stories in this article show, has created profound consequences that have been well-hidden from all of us.
• This article will explore how this dysfunctional dynamic has harmed the health of America, meaningful changes that could preserve the vital functions of the FDA while simultaneously preventing it from sabotaging America’s health, and the changing political winds we’ve helped create which are gradually forcing those changes to happen.

The majority of decisions in life aren’t clear cut as they have both an upside and a downside (or multiple upsides and downsides). However, rather than being fully cognizant of the complexity of the decision, the human mind will typically narrow the picture and see only one side of the coin to reduce this large cognitive load. Many perpetually unresolved political conflicts essentially result from this, as each side emotionally primes their adherents to focus on the arguments in favor of their position and those which undermine the other side, resulting in both sides having a view of reality where their side is correct and the other is irredeemably wrong—which in some cases holds true, but typically is not.

One of my favorite frameworks for encapsulating this paradigm is the biostatistics concept of “sensitivity and specificity,” which denote how likely a test is to catch something that is there (sensitivity) and how likely it is not to overshoot and only identify things that are actually there (specificity). The value of this framework (beyond providing an informed way to choose medical tests) is that it emphasizes the reciprocal relationship between the two, as if one is increased (e.g., more aggressively screening for something), the other decreases (e.g., that screen will have a higher rate of false positives).

Because of this, ideally, the sensitivity and specificity of a test (and what will then be done with either result) should be appropriate to a patient’s clinical situation and in parallel, work is always done to improve the tests themselves so better balances between sensitivity and specificity can be met. In contrast, in overly politicized issues (e.g., criminal justice), the focus always ends up being on maximizing sensitivity OR specificity rather than finding a reasonable compromise between the two, which maximizes both as much as is feasible.

However, the reality is that you will frequently be confronted with situations where there is a less than ideal balance of upsides and downsides (e.g., sensitivity and specificity) between the two options, but a choice nonetheless must be made. Fortunately, due to how common these situations are, effective decision making strategies have been developed and refined.

Predicting Expected Values

The classic mathematical formula used to “solve” these situations is expected value (EV), which essentially calculates “on average, how much will this situation benefit or harm me.”

This translates to (magnitude of outcome 1 * probability of outcome 1) + (magnitude of outcome 2 * probability of outcome 2), and this is repeated for all possible outcomes (e.g., it could go to calculating outcome 10) so that the total probabilities add up to 1. So for example, if you had a situation where you paid a dollar to flip 2 coins and then got 99 cents for each “heads” you got, the EV for the four outcomes (HH, HT, TH, TT) would be (1.98*0.25)+(0.99*0.25)+(0.99*0.25)+(0*0.25) or $0.99. Given that your cost to play this game is $1.00, it is hence “not a good idea” to play the game as there is a negative DV (on average you will lose money).

Many businesses (beyond just casinos), in turn, are essentially structured so that the EV of the transactions they make are positive for them (and negative for the customer) hence (excluding highly unusual circumstances) ensuring a steady stream of profit which sustains the business or industry.

While everyone has a general grasp of EV (e.g., if you saw a dollar bill fly into the freeway, almost no one runs onto the highway to try to grab it as the risk of being hit by a car makes the EV very bad there), a few points are critical to understand about it:

First, most people do not have a strong grasp of probabilities, and as such, predatory industries will frequently mislead them about the actual probabilities, leading them to believe bad EV choices are actually good EV choices.

Second, rather than being a simple binary calculation, EV calculations are often complicated because there are many potential outcomes (variables).

Third, EV can encompass a variety of outcomes beyond financial gains or losses, at which point it becomes harder to fit into a numerical formula.

For example, many of the policies that were pushed on us during COVID-19 essentially arose from people being implicitly presented with erroneous EV formulas by the mass media, and then extrapolating decisions off those formulas which appeared beneficial (positive EV) but in reality were harmful (negative EV) with a correct formula.

To illustrate, the odds of a child dying from COVID-19 were effectively 0 (and in the small number who died, there was almost always a severe underlying condition), so no real benefit could be derived from vaccinating, whereas injuries (including fatal ones) routinely occurred. So, the EV of a child taking the COVID vaccine was always negative (as there was no “positive” outcome, whereas a negative one could occur).

Likewise, when the Pfizer NEJM paper came out (which made many, including most of the medical profession, decide they had to get the vaccine no matter what since it was “95% effective!” and would end the pandemic), the paper itself stated:

1. Adverse reactions were much more common in the vaccine than the placebo group (27% vs 12% for a direct event and 21% vs. 5% for an unrelated adverse event)

2. In contrast, for COVID infections, those same symptoms occurred, typically 1-2 times as frequently (sometimes 3x).
3. 8/18,198 (0.044%) vaccinated developed COVID and 162/18,325 (0.88%) of the unvaccinated developed COVID (a 20-fold decrease).
4. Seven days after the initial dose, 1/18,198 vaccinated and 9/18,325 unvaccinated developed severe COVID (COVID typically requiring hospital care).
   Note: this metric was changed to after the first vaccine (whereas the primary efficacy measurement ones were after the second vaccine), since 5 of the severe infections in the placebo group happened prior to the seven day post vaccine cut-off (which was hidden in the appendix). Had this standard been used for all COVID infections too, it would have been (39+8)/18,198 vs. (82+162)/18,325 (a 5.16 fold decrease falling far short of the “95% effective” benchmark), whereas had the study’s primary criteria been used here, it would have been 1/18,198 vs. 4/18,325 (a 4-fold rather than 9-fold decrease)—illustrating how studies always change their metrics and criteria in whatever manner makes the product look best.
5. 4 serious adverse events attributed to vaccination were reported (shoulder injury from injection, right axillary lymphadenopathy, paroxysmal ventricular arrhythmia, and right leg paresthesia).
6. 2 vaccine recipients died and 4 placebo recipients died (all from causes unrelated to COVID-19).
7. Nothing in this study evaluated transmission.

When I read this paper, I was jaw dropped, as it was blatantly stating there was an extremely negative EV for the vaccine as you were trading the symptoms from a COVID infection for a 1/119 chance (0.88%-0.044%) of not getting COVID, so if you assumed COVID-19 symptoms on average were twice as frequent as vaccine symptoms you were increasing your likelihood of getting ill 60-fold by vaccinating (along with the injection site specific symptoms only seen from the vaccine) in return for a possible halving of COVID symptoms (although in reality, people often felt far worse post-vaccine than during COVID). If you attempted to counterweight that by the major benefits of vaccine, the most important one, death, was not prevented, while the medium one “severe COVID” had required between 2,293 to 6,123 vaccinations to prevent one instance (with the higher figure arising if a consistent metric had been used by Pfizer), and the only other possible justification for vaccinating (reducing transmission) had not been tested in the paper.

Furthermore, since fairly consistent methods are used to doctor papers, I was relatively certain:
•Vaccine efficacy had been overstated (e.g., COVID cases in vaccine recipients were not reported) and severe injuries in vaccine recipients also were not reported—both of which were later corroborated by numerous trial participants and trial researchers.
Note: while the trials were happening (inspired by what I’d learned happened in the Gardasil trials where many of the reported adverse events were magically erased), I joined online support groups for the trial participants and noted that many of the adverse events they reported did not appear in the final trial report, and that the overall severity from a reaction to the vaccine was significantly worse than what I typically saw people experience with COVID-19. My suspicion adverse reactions were covered up in the trial solidified once the vaccine hit the market, because almost immediately, I had multiple patients each day seeking help for severe and unusual vaccine reactions and people I knew from around the country began contacting me to ask if the vaccine could cause strokes or heart attack (as it had happened to someone they knew)—and most importantly, my sample size for these early reports was far smaller than the 18,198 vaccine recipients in the trial.

•Any benefits not reported in this paper (e.g., transmission or reducing death) would never be found for this vaccine as every possible attempt had been made to exaggerate the benefits and they could only decline from this point forward (e.g., before long everyone would have immunity to the original strain and COVID-19 would mutate to something no longer covered by the vaccine).
Note: at a six month follow up, deaths were slightly higher in the vaccinated than the unvaccinated group. Likewise, despite there being no evidence that the vaccine prevented transmission (and its symptom-reducing design arguing against this even being possible) health authorities and the media widely promoted the vaccine as preventing transmission to pressure people to vaccinate, until real life data forced them to backtrack on their claim.

•Flipping the criteria for severe COVID-19 (compared to minor COVID) to make the vaccine look better demonstrated that data manipulation was occurring in the paper (hence casting everything else into doubt). Later, as I started noticing a lot of people become severe ill with COVID-19 (and in many cases dying) immediately after vaccinating (including individuals who had minor PCR confirmed asymptomatic infections), I realized this issue had most likely been detected by Pfizer and hence why the criteria for evaluating COVID hospitalizations was changed to “seven days post the second vaccination” (which resulted in many vaccine COVID-19 deaths being labeled as “unvaccinated” deaths).
Note: “disease provocation” due to vaccine-induced immune suppression is a longstanding problem with vaccination (e.g., a good case can be made many of the pre-polio vaccine polio outbreaks were due to vaccination, COVID-19 was the two most common fatal COVID vaccine reactions reported to VAERS and longitudinal data showed the more COVID vaccines you got the more likely you were to get COVID-19)—all of which is discussed here.

Put differently, my immediate thought after looking at the paper was that if after all their best attempts to make the vaccine look as good as possible, it was still this bad, it meant the actual data was likely appalling. Remarkably however, when I discussed this paper my physician colleagues, they could only “see” the 95% effective figure (the 20-fold relative reduction) and all the other points I raised, which were in the paper, went in one ear and out the other, hence illustrating that most people simply do not have a good grab on probabilistic reasoning (outside of those in competitive fields where optimizing EV choices is necessary for success).

Note: the formula which goes hand in hand with EV is Bayes’ theorem [P(A|B) = [P(B|A) × P(A)] / P(B)], which provides a method for updating the probability of something being true as new evidence becomes available. In medicine, it is essential for correctly interpreting diagnostic tests (e.g., understanding that a positive result from a screening test in a low-risk population is more likely to be a false positive than a true one), yet remarkably few physicians actively apply it in their clinical reasoning^1,2—which in turn leads to a significant amount of overtesting (some of which in fairness, they know is not justified but is done to avoid potentially being sued), overdiagnosis, and unnecessary treatment.

Finally, it should be noted that the EV of the COVID vaccines was much easier to calculate than that of most other vaccines in use (because there was a much smaller set of variables and much more available data on those variables). For example, to begin calculating the benefit of a routine vaccination, you first need to start with:

Then you need to weigh that against the risks of each vaccine in the series (as later ones typically cause more injuries), with separate calculations done for each degree of injury severity, along with subgroup susceptibility (as some people are much more sensitive to vaccine injury than others) and then once that is done, somehow assess the cumulative effect of all the different vaccines being taken (as vaccine toxicity and immune dysregulation are cumulative). However, rather than try to engage in that complex calculation, the medical industry’s solution has been to assume all vaccines are “magically safe and effective” and like the COVID vaccine, give both incredibly optimistic models of efficacy while only focusing on a few inconsequential reactions (e.g., temporary injection site reactions).

As such, much in the same way doctors were convinced the COVID-19 vaccines would end COVID because it was “95% effective” (when nothing of the sort then happened) and that the vaccine was much safer than getting COVID (despite trial data indicating the opposite), virtually no knowledge exists on the actual EV of most vaccines because they were given a simplified formula to calculate them which only highlights a few key variables industry wants people to focus (which arrive at a high EV). This sales strategy, in turn, is quite effective as it allows people to avoid the hard mental work of having to complete a complex calculation (hence appealing to human laziness), while simultaneously appealing to the human ego by providing the illusion of mastery and authority in the area (by regurgitating the simple arguments used to authoritatively enshrine a positive EV for the vaccines).

Note: a while back I tried to calculate the risks and benefits of each childhood vaccine (as they vary immensely with some being much worse than others)—all of which is detailed here.

Lastly, it should be acknowledged that the original emergency use authorizations for the COVID vaccines were granted under the premise that no other treatment existed, the vaccine’s massive potential benefit justified the existing uncertainty over its effectiveness, and that authorization could be modified as more data emerged. However, not only did other treatments already exist, the FDA then shredded the EV of the vaccines by continually doubling down as more and more evidence of ineffectiveness and harm accumulated.

Stagnating Science

The following holds true for our current society:

•It highly values science and scientific truth to the point that many people worship it in place of religion.

•In order for science to be “valid” (and widely promoted by the media) two bars typically must be cleared—a large randomized control trial is conducted that arrives at a statistically significant corroborating outcome and the scientific authorities must bless a given scientific conclusion.

In some cases, this is a very helpful framework, but in many instances, it is extremely vulnerable to abuse. This is because:

•Large RCTs are extremely expensive (tens of millions of dollars), to the point that they typically can only be financed by national governments, massive pseudo-non profits (e.g., the Gates Foundation), or pharmaceutical companies.

•Any controversial study that somehow makes it through that ideological filter will still often be routinely dismissed by the medical authorities (and in many cases retracted once too many people start citing it).

•Studies that do not meet this threshold are very easy to dismiss, and will virtually always be dismissed if they arrive at a conclusion that threatens a major interest.
Note: it is very common for the abstract or conclusion of a study to provide a summary which contradicts the study’s results if the actual data is “politically incorrect” or “undesirable” (as most people only ever read summaries). Fortunately, AI now makes it very easy to expose this tactic.

•Since most scientists are dependent upon either grants or pharmaceutical funding (the only two sources of funding for costly research), they quickly learn they cannot pursue “controversial research” and hence do not produce research that will tank the rest of their career.

Because of this, we’ve run into a situation where most research is highly conservative and incrementally builds upon existing discoveries rather than making new revolutionary discoveries which advance science and change paradigms. For example, this is how Gerald Pollack aptly described our current situation.

from:  https://www.midwesterndoctor.com/p/the-critical-calculation-medicine?publication_id=748806&post_id=196647596&isFreemail=true&r=19iztd&triedRedirect=true&utm_source=substack&utm_medium=email