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Houston, The CDC Has a Problem (Part 2 of 3)

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As we survey the sizeable array of loose-end and speciously categorized data, it becomes readily apparent that the CDC is exhibiting all the symptoms of an organization which is constrained under the burden of a set of Kuhn-paradigm walking dead theories regarding Covid mortality.

Official data compromised so as to portray disinformation, is the warning sign that an entity, ostensibly one granted a government-authorized monopoly, under joint action to serve the American Public, is no longer serving science nor their fellow citizen – rather only social doctrines and out-of-control politics.

American philosopher Thomas Kuhn is credited with the proposition that science does not evolve gradually towards truth, but rather tends to anchor itself to a paradigm – a construct, notion, or hypothesis which bears the risk of remaining in play long past its shelf life. A theory thus can metastasize into a type of cult, zombie, or walking dead notion, if you will. Kuhn proposed that science therefore advances by what he called a paradigm shift and not merely by gradualism, nor especially through accretion of a set of conforming and convoluted explanatory gimmicks.

Such paradigm shifts occur in the particular circumstance where a current theory cannot sufficiently explain a phenomenon, and a coherent set of counter-observations have begun to accrue. A scientific revolution occurs when: (i) a new construct can be inferred directly from a set of these counter-observations; (ii) this novel paradigm offers superior explanatory power regarding the objective, observed reality; and (iii) the new paradigm runs heterogeneous to established (zombie) theory or narrative.1

A zombie theory is often times one which is sponsored and enforced by a syndicate. It will tend to be flagged for proactive support by its philosophical sycophancy, associated social movement, or allied political party. Such activity of course lends no credence whatsoever to the theory’s actual scientific validity.

When protection of a syndicate-sponsored idea becomes more important than the integrity of science itself, this is a particular form of zombie theory which ethical skepticism calls an Omega Hypothesis.

Within a previous article, we identified a mode and form of inference which we coined as heteroduction.2 Heteroduction is the very process of scientific inference which undertakes step (i) of the scientific revolution cycle identified by Kuhn. Heteroduction becomes of paramount importance in the presence of an enforced Omega Hypothesis.

A corporation or a political movement can become so fixated upon an established zombie theory, that its prevailing elements can rule as a form of pluralistic ignorance inside corporate ranks for years or decades – especially inside entities which do not operate in a market, and lack public scrutiny or competition. The entity or corporation will adopt a form of willful blindness toward its own foibles and fraud in support of its Omega Hypothesis. It will fail to self-check, begin to undertake borderline or even fully unethical activity in order to control what is known, and finally seek to actively suppress any form of dissent inside its ranks.

What the reader will observe below are a series of observations, a heteroduction if you will, signaling the presence of several Omega Hypotheses at play inside the US Centers for Disease Control and Prevention (CDC) – specifically the notion that Covid-19 has itself served as the sole origin of all the observed excess mortality in the US, and that we now face merely the aftermath of Covid-19’s wake, in the form of a pseudo-theory called ‘Long Covid’. A pseudo-theory is a mere notion enforced as science, which explains anything, everything, and nothing, all at the same time. Both of these notions have been falsified in spades. As we begin to examine and pull on the large set of tattered loose threads in the form of the database anomalies exhibited below, it becomes apparent that the CDC is exhibiting all the symptoms of an organization which is operating under the burden of a Kuhn-paradigm walking dead theory; and moreover, a politically-fueled Omega Hypothesis.

CDC MMWR Reporting Problem-Indicator Flags

The principal concerns with regard to the US Centers for Disease Control and Prevention Weekly Provisional Counts of Deaths by State and Select Causes and Wonder: Provisional Mortality Statistics are that the reports have begun to exhibit two primary apparent goals on the part of the CDC and its agency:

  • concealing excess deaths potentially caused by the mRNA vaccines, and
  • attempting to make mRNA vaccines falsely appear as uber-effective in saving lives.

Please note that we will not resolve an answer to either of these issues in this article, rather herein we will only outline the efforts in disinformation, misinformation, and deception on the part of the CDC which are foisted in an attempt to achieve both goals.

Accordingly, four key issues are entailed inside this two-sided-coin deception:

  1. The National Vital Statistics System Upgrade (hereinafter referred to as the ‘NVSS System Upgrade’) afforded the CDC a timeframe inside which it could alter 22 weeks of NCHS-MMWR data. During this window of opportunity the CDC surreptitiously removed excess death records from its database, and adjusted the policies and techniques as to how ICD-10 mortality codes were populated with state death certificate data thereafter.

We outline herein that a new policy was enacted during the NVSS System Upgrade break, one which centered around two categorical gaming practices. The CDC is employing categorical gaming techniques to conceal dramatic Excess Non-Covid Natural Cause Mortality. If these excess deaths are not Covid deaths and are not vaccine related, as is commonly claimed through appeals to authority, credential, and ignorance, then there should also be no reason to conceal their associated records. Yet, that is exactly what is occurring.

  1. Excess Cancer Mortality is being concealed through Cancer Multiple Cause of Death (hereinafter referred to as ‘MCoD’) categorical reassignment to Covid-19 Underlying Cause of Death (hereinafter referred to as ‘UCoD’).
  2. Sudden Adult Deaths are being concealed by holding Pericarditis-Myocarditis-Conductive heart related deaths inside the R00-R99 temporary disposition bucket, far longer than per historical practice, thereby falsely depleting the associated ICD-10 mortality trend for these related deaths.

Finally, the CDC is using the exact opposite technique, exploiting Multiple Cause of Death attributions and adding in completely fictitious deaths as well, in order to make its mRNA vaccines appear to be performing better than they are.

  1. The CDC is using Multiple Cause of Death categorical gaming, and is creating novel death counts, in order to counterfeit an appearance that the unvaccinated are dying at a rate twelve times that of the vaccinated.

These four issues are detailed as follows.

1. The NVSS System Upgrade Provided an Opportunity to Short and Reassign Death Records

The upgrade of the National Vital Statistics System (hereinafter, NVSS System Upgrade) was Machiavellian in its timing and opportunistic focus. In fact, as of October 2022 the entire evolution appears to have been a charade, crafted merely to obfuscate the set of warning indicators and activities outlined in this article. The NVSS System Upgrade provided an opportunity for the CDC to develop mechanisms to conceal Sudden Adult Deaths and Cancer Deaths (see Exhibit 1B below), and ironically only served to degrade the externally observable overall function and performance of the NCHS/State to CDC reporting process. Ostensibly, the process of final ICD-10 state death certificate record classification was to become tighter as a result of this upgrade. In the end, such benefit failed to manifest, as only Cancer Mortality reporting classification-lag actually appeared to improve. Yet even this ‘improvement’ in lag time turned out to be nothing more than the result of the CDC working to quickly hide cancer deaths in the first place (as documented in Exhibits 2A through 2D below). Overall, the NVSS System Upgrade was a failure – and only served to provide cover for surreptitious activity on someone’s part.3

Exhibit 1A – The NVSS System Upgrade was announce on June 6th. Originally, only two reporting periods were to be impacted. As it turned out, seven were impacted, along with 22 weeks of shorted mortality data reporting.

The NVSS System Upgrade was slated to last 2 MMWR reporting weeks, yet ended up lasting a full 5 weeks longer than planned – thereby returning to its market with a full slate of shorted death records inside two specifically targeted ICD-10 code mortality sets, per Exhibit 1B below.

Exhibit 1B – The NVSS System Upgrade period was exploited in order to reduce, in particular over all other ICD-10 mortality codes, sudden adult deaths, cardiac-related and conductive disorder deaths, and finally cancers. Notice how the regular lag-accretion of records occurs in all timeframes both before (A) and after (B) the period in question – yet in contrast, the period in question remained permanently shorted.

As of the last tally we conducted regarding records lost during the System Upgrade, of the 51,910 records which disappeared from the data during the seven week hiatus, 13,245 were reassigned to other ICD-10 death codes, while 9,290 records remained missing from the database altogether. 70% of these missing and reassigned records involved death certificates pertaining to Sudden Adult Deaths and Cancer Deaths. This was not accidental in the least. Those two ridiculously negative-impacted ICD-10 death charts are shown in Exhibit 1C below. Although they are silenced now by the successive weeks confirming that we were correct in our assessment, there were Narrative-driven persons who reviewed my material and insisted that I use these figures as fact in my analyses (falsely touting such condemnation as ‘peer review’). I refused on the basis that I will not succumb to publishing Narrative disinformation.

Exhibit 1C – A detailed audit of the missing death records resulting from the NVSS System Upgrade, shows that around 70% of the missing deaths pertained to Sudden Adult Deaths and Cancers. This should not have been the case if the drop in death cases were a mere temporary artifact or an aspect of newly-showing and compressed lag (improved system functionality), as was claimed. This record shortage served to falsify those notions.

Once this period cleared, and successive MMWR weeks were entered into the database, it became readily apparent that these sudden dips in mortality during the System Upgrade (the two right hand charts in Exhibit 1C above), were either erroneous or fraudulent in nature. Exhibits 2D and 6 show the corrected charts, as we continue below.

2. Categorical Concealment of Multiple Cause of Death Cancer Mortality

The NVSS System Upgrade afforded the CDC opportunity to both manipulate and excuse its reporting of cancer mortality, in order to obfuscate the 9-sigma excess in this ICD-10 code (C00-C97), a trend which had manifested early in 2022. The first signs of this obfuscation effort manifested immediately after the close of the System Upgrade, through a compression observable in the Cancer mortality death lag curve, per Exhibit 2A below. At first we attributed this lag curve compression to an actual improvement in record service time-to-data, as the CDC had indicated was the entire purpose of the NVSS System Upgrade.

Exhibit 2A – Out of the gate at the end of the NVSS System Upgrade, the CDC data drops began to exhibit a separate lag profile for Cancer, versus all other ICD-10 code mortalities. It appeared that cancer was being reported through a separate system from all other state death certificate reporting functionality. As it turned out, as shown in Exhibits 2B through 2D below, the CDC was merely reassigning Cancer Multiple Cause of Death records as Covid-19 Underlying Cause of Death, when Covid-19 was on the MCoD listing on the death certificate (they did this 100% of the time, equaling around 350 – 450 deaths per week).

As it turned out, this ‘service time improvement’ presented as nothing more than a ruse. The cancer mortality lag curve compression which had been observed in Exhibit 2A above was merely an artifact of records being removed from the ICD-10 tally for cancer UCoD, occurring from week 2 through 18 of the provisional death lag period. In other words, the reason cancer deaths were hitting their long-term figure levels as soon as week -4 (all other ICD codes took 12 weeks to accomplish this), was because cancer deaths were being reassigned to Covid-19 UCoD deaths (see Exhibit 2D), or were being removed from the data altogether (see Exhibit 2E) after week -2 (per Exhibits 2B though 2E below). No wonder the lag cleared so fast.

Exhibit 2B – When one matches the MCoD-Only Cancer Death count Covid Deviation from Trend to the UCoD Cancer Only Deviation from Trend, once can observe that 100% of the MCoD Cancer-Covid death records are being given a Covid-19 Underlying Cause of Death.

In order to test just how ludicrous this reassignment of Multiple Cause of Death data is, in Exhibit 2C below one may observe that this quotient of cancer death reassignment to Covid-19 UCoD was not well thought out by the CDC at all. They left a loose end, an Irish Pennant, hanging about – and we caught it.

Since the NVSS System Upgrade, a full 25% of all Covid-19 mortality each week has just happened to be people also dying of cancer. Such constitutes an impossibility in this important mortality account ledger, one which is analogous to the same species of mistake an embezzler might make.†

(† Please note that I have had more than my share of embezzlers caught and intelligence cases broken during my career. I am well qualified in this professional activity.)

Exhibit 2C – Multiple Cause of Death Cancers with Covid-19 as a Percent of Total Covid Deaths. In 2022, post the NVSS System Upgrade around one-quarter of all Covid-19 death victims, suddenly also happened to be dying of Cancer. This was not the case throughout any other period during the pandemic – the ratio normally falling around 5.3% (in itself also high). The only way this over-apportionment can happen is if Covid is being assigned trivially to cancer patients, so that Covid-19 may further then be assigned as the Underlying Cause of Death, thereby reducing the ICD-10 Cancer tally accordingly. In other words, a categorical gaming of cancer death tallies. This was an undisclosed policy change which occurred during the System Upgrade.

The net effect of this nefarious activity has been a shorting of 350 to 450 Cancer Underlying Cause of Death records from the ICD-10 database each week since the NVSS System Upgrade. When those death records are added back into the data (as they were prior to the System Upgrade) the Cancer Mortality trend resumes its 9-sigma cancer death excess which was observed immediately prior to the System Upgrade, and the cancer provisional lag comes back into alignment with the lag observed inside all the other ICD-10 codes, quod erat demonstrandum.

Exhibit 2D – If one removes merely 75% of the MCoD Cancer/Covid as a percentage of total UCoD Covid-19 mortality, an anomaly which manifested only after the NVSS System Upgrade, and assigns these deaths correctly back into a Cancer UCoD – then suddenly ICD-10 Underlying Cause of Death Cancer Mortality lag comes back into alignment with all other ICD-10 Code mortality and Cancer Mortality returns to its 9-sigma excess level, the level it had attained when the NVSS System Upgrade was declared (manifestly ignorant or malicious timing). Accordingly, this is proof that Cancer deaths are being concealed by the CDC as Covid-19 deaths.

Finally, as one can see below, not satisfied with a mere re-assignment of cancer deaths over to Covid-19 death tallies, the CDC took this one step further and simply removed another 40 to 75 cancer death records from the MMWR database altogether, each and every week of the last 18 weeks. This broaches the question, is such a record reduction then normal? The answer to this question is an unequivocal ‘no’. Almost 100% of the provisional death records end up rising, not dropping, during the lag and provisional reporting period. This drop in cancer deaths is indicative of an exception activity at play on the part of those managing the MMWR reporting databases (Wonder data in the case cited in Exhibit 2E below). By itself, this might not mean that much. However, in light of the full set of nefarious activity centered on obfuscating both SADS and Cancer mortality, this exception too is indicative of fraud underway.

Exhibit 2E – The CDC is removing 40 to 75 cancer deaths from the MMWR reporting database altogether, each week since the NVSS System Upgrade ended (grey line marked down to the orange or blue line as applicable – highlighted inside the red circle). This indicates both an undisclosed policy change during the System Upgrade period, as well a desire to obfuscate as many Cancer deaths as a shrug-of-plausible-deniability might allow. This cancer death certificate removal activity is occurring as of MMWR Week 40 of 2022 up to even 18 weeks after the attending physician or hospital has filed the final death certificate.

One can confirm these desperate attempts to obfuscate Cancer Mortality data by observing the ICD sub-sub code for Ill-defined and Secondary Site (C76-C80) cancers as well. This is an ICD-10 sub-sub code which normally makes up around 15% of all Cancer deaths each week. Post MMWR Week 14 2021 this sub-sub code suddenly composes 42% of all novel Excess Cancer Mortality. One can observe, by accessing this Cancer (C76-C80) meta-chart, that the CDC is particularly concerned about obfuscating the data for this category of entropy-indicating Cancer Mortality.

One can also confirm such false Covid-19 UCoD attributions, by observing the stark but false rise in case fatality rates in the US in late 2022, as is depicted in this US Case Fatality Rate chart. Comparatively, the same chart for Europe features no such rise in CFR (converging around half the CFR of H1N1 flu, in line with most human coronavirus CFRs). Of course therefore, the World does not exhibit this CFR rise either. Only the US is stoking fear and hiding panic mistakes by means of false death accounting.

3. Categorical Concealment of Sudden Adult Death (Pericarditis/Myocarditis/Conductive Cardiac) Mortality

A paradox exists with regard to sudden young person and adult cardiac or anomalous deaths observable since mid 2021. Tens of thousands of cases of young persons dying suddenly in their sleep, or after a sporting event, are belied by a purported reduction in sudden cardiac death claimed by the CDC and pharmaceutical industry trolls, since the NVSS System Upgrade (see Exhibit 3B top panel).

One of my kids’ group of friends, a healthy young man who just finished college and was filled with hopes and dreams, suddenly died in his sleep of conductive heart failure several months ago. No drugs or alcohol were involved.

We were asked to believe (and it is only a belief), that these deaths ‘happen all the time’ and we were just not paying attention to them before. Bullshit – this is the same type of farce which was played upon us as parents of a child newly diagnosed with permanent disability encephalitis from vaccine injury years ago. I recognize the shtick. This is gaslighting.

In fact, those who enforce such pseudo-theory (remember, a theory which explains everything, anything, and nothing, all at the same time) hold absolutely no data to support their narrative dogma. It is maliciousness, pretending to be helpful. Below, we demonstrate why such activity constitutes gaslighting.

In Exhibit 3A below, one can observe that a temporary bucket exists which holds abnormal clinical and lab finding deaths (heavily represented by myocarditis, pericarditis, and conductive heart disorder deaths – because these are the deaths which most often serve to baffle doctors and coroners – as was the case with our family friend). Such deaths are not to be conflated with fentanyl or drug abuse deaths – which are easily detected through blood testing and are accounted for separately.

During the period prior to MMWR Week 14 2021, to include the pandemic period, these deaths were resolved 90% to their final ICD-10 disposition across about 3 to 12 weeks. As one may observe in Exhibit 3A below, not only has this bucket of deaths grown by 70% since the introduction of mRNA vaccines into the US population, but as well, the CDC has decided to cease resolving these deaths to their final ICD-10 disposition. This has resulted in an estimated 35,600 abnormal clinical and lab finding pericarditis, myocarditis, and conductive disorder deaths which are not being accounted for in US Cardiac Mortality – thereby artificially depressing those ICD-10 mortality trend curves and allaying the conscience-nightmares of the pharmaceutical executive board members of the CDC.

This too, is no different than embezzlement of expense money or tax fraud inside a corporation or charity accounting ledger. Jim and Tammy Faye Baker would be impressed.

Exhibit 3A – Abnormal clinical and lab findings deaths not only increased by 70% in the window immediately following the introduction of the Covid-19 mRNA vaccines, but also have not abated. In June of 2022, commensurate with its ‘System Upgrade’, the CDC began using this R00-R99 (primarily the R99 code in particular) as a repository to conceal sudden adult deaths from arriving into their final ICD-10 code disposition – thereby concealing the escalation in pericarditis/myocarditis/conductive heart deaths among especially young adults. As of MMWR week 40 of 2022, 35,600 excess deaths remain concealed inside this temporary disposition bucket.

If one assigns a mere 18% of these anomalous and heavily cardiac deaths in younger persons, back to the Wonder data concerning myocarditis, pericarditis, and conductive heart disorder deaths – one gets a 22-sigma increase in this mortality sub-group since MMWR Week 14 of 2021. This process is shown in Exhibit 3B, and the result is indicated in its lower panel. In fact a very stark inflection in this data develops immediately commensurate with the roll-out of mRNA vaccines nationwide in the US.

My fear is that far more than 18% of these bucket-hold and unresolved deaths, involve abnormal and clinical findings related to pericarditis, myocarditis and conductive heart disorders – and the CDC is concealing a tsunami of a problem. This is a human rights crime.

Exhibit 3B – If one takes a mere 17.8% of the suspended R00-R99 Symptoms, signs & abnormal clinical and lab findings deaths, many or most of which are sudden myocarditis/pericarditis/conductive-heart related in nature, and assigns those deaths to Myocarditis-Pericarditis-Conductive and Other Heart Related Mortality (I30, I40 and Others) – a frightening trend in this mortality category arises.

In fact, if we prosecute this very inference which we inductively derived above, and test this deductively by querying the Wonder MCoD data for all Cardiac related deaths, including those RXX Abnormal & Clinical Lab Findings deaths which relate to abnormal heart-related conditions4 – those ostensibly held in the 22 week stasis cited above – one finds an alarming result. In the cases where there is no Covid-19 listed on the death certificate, we are at an all time high in heart-related deaths for the entire pandemic period. In fact a 17-sigma high (20-sigma with pull forward effect taken into account).

Exhibit 3C – Diseases of the Heart (IXX) and Uncertain Related Disorders (RXX) are at an all time high as of MMWR Week 38 of 2022. Yet you will find pundit after pundit inexpertly declaring that we do not have a problem with heart-related deaths. I have never seen a 20-sigma run in an ICD-10 code with this many weekly deaths attributed to it as a portion of overall mortality. This is extraordinary.

These excess Diseases of the Heart (IXX) and Uncertain Related Disorders (RXX) are not a result of Covid itself, despite the 2020 sympathy curves (Exhibit 3C 2020 blue line humps are in reality missed Covid and not a true increase in this ICD-10 code).5 This inflection and post-inflection signal should be lighting off warning alarms in all corridors of public health, … yet it is not. The combination of consilient inductive strength, reduced by convergent deduction – is tantamount to proof. Sorry pundits, your appeals to ignorance have only served to harm people.

4. Categorical Exploitation of Covid-19 MCoD Mortality to Coerce the Public with Case Fatality Rate Disinformation Regarding Vaccine Effectiveness

In Exhibit 4A below as well, one lays witness the accounting wherein suddenly, Multiple Cause of Death Covid-19 deaths are all assumed to be Underlying Cause of Deaths in solely the unvaccinated cohort. For the week analyzed in Exhibit 4A below, the Covid-19 MCoD tally for the week was 2,650 MCoD deaths (in the over-50 age bracket). Thus the CDC had to convert 841 MCoD deaths to UCoD deaths, and assign them only to the unvaccinated, and manufacture an additional 548 deaths more in that same cohort, in order to make the unvaccinated appear to have a 12 x case fatality rate as compared to the vaccinated cohorts. Rather than being executed inside a database however, this sleight of hand was accomplished in a sampling study instead – one where a reverse projection check was never done, in order to make sure that the results of the inductive projection study were sound – or in this instance, even possible.

These results are not mathematically possible.

This is coercion of innocent citizens by means of purposeful disinformation, and constitutes fraud.

Exhibit 4A – Invalid incorporation of Multiple Cause of Death (MCoD) records to inflate the Unvaccinated Cohort apparent death rate. If one does a reverse projection soundness check on the cohort mortality rates published by the CDC (left two panels in chart) for the week of 10 July 2022 and compares that to the actual MMWR Report deaths (over 50 years of age) for that same week, one finds that the rates of mortality require an addition of 77% more deaths – all of which are added into the Unvaccinated Cohort by the sampling studies referenced.

Below in Exhibit 4B, we take the set exclusion calculations above in Exhibit 4A and portray them along the timeline of their arrival (23 weeks from 2 Apr to 3 Sep 2022), using the CDC’s very own analysis regarding death rates among 50+ year old vaccinated and unvaccinated cohorts.6 When these model calculations are extrapolated to the entire 50+ US population, suddenly a superfluous 12,656 death-count appears – conveniently in the timing and arrival form necessary to comprise the entire excess unvaccinated cohort death component. The superfluous deaths far exceed the 6,617 deaths necessary in fabricating the difference between the unvaccinated and vaccinated cohorts. The calculation base for Exhibit 4B below, which extends from the CDC model, can be seen by clicking here.

Exhibit 4B – By calculating the superfluous deaths used to create the CDC’s fatality rates by vaccinated and unvaccinated cohorts (upper panel), one can observe how these manufactured deaths in the sample populations studied (many of them, but not all, questionable MCoD ascriptions) can be, and only can be, exploited to create the entire vaccinated death rate differential necessary (blue line vs brown line in the lower panel). In fact the arrival form of these superfluous deaths matches exactly the surge in unvaccinated cohort deaths. Note the 18-day separation between the two ‘death’ arrivals.

There is no doubt therefore, that the CDC and/or the surveillance hospital networks feeding this analysis, have manufactured superfluous deaths and inserted them in to the unvaccinated cohort death rolls, in order to fabricate a misleadingly high fatality rate among the unvaccinated as compared to the vaccinated. Inside this activity, the 18-day gap (shown in the upper panel of Exhibit 4B) between the vaccinated and unvaccinated cohort deaths is pivotal. The gap suggests that the following protocol was used to fabricate this cohort differential. Condition #1 below conforms with case arrivals and not the actual arrival of Covid deaths (18 days later).

  1. Unvaccinated status + any UCoD + nosocomial Covid/no Covid = ‘Covid’ death
  2. Vaccinated status + Covid + any MCoD = definitely not a Covid death (use the MCoD)

In other words, die of anything yet also be unvaccinated – then you ‘died’ of the suspected Covid you ‘caught’ upon entering hospital or hospice. Hence a peak sympathetic with cases, 18 days early. If vaccinated – then one could not possibly have had Covid, nor especially severe-Covid, therefore one didn’t die of Covid. A self-fulfilling model which functions upon the circular logic of constraints alone – one gets only that for which they constrain.

Moreover, the superfluous deaths quantified above in Exhibit 4B cannot be reconciled in any other fashion when constrained by the CDC model published above. The only place this magnitude of death differential can be accommodated in the CDC model, is by stuffing the superfluous death count for the period inside a skewed comparative – and the unvaccinated cohort death rate is the only one large enough to accommodate this large superfluous death count.

Thus, the unvaccinated death counts are falsely inflated and the cohort differentials are fraud, quod erat demonstrandum.

Addendum: The Yule-Simpson Effect Inside All Cause Mortality

Finally of course, it should be noted that the CDC attempts to define a pandemic and furthermore expresses its pandemic updates, in terms of All Cause Mortality. As epidemiological professionals in this case, they should not be using such a misleading metric. Pandemic risk at this juncture needs to be evaluated in terms of Excess Non-Covid Natural Cause Mortality (see Exhibit 6 below). This metric (the contrast of which can be observed in Exhibits 5 and 6) offers an indication of risk from Non-Covid death causes. As one can see in Exhibit 6 below, Excess Non-Covid Natural Cause Mortality is at an alarming 13.3% excess, while All Cause Mortality Excess (shown in Exhibit 5) stands at a rather nominal 3.3%. All Cause Mortality can therefore be deceptive when used as a stand-alone metric. Reader, be cautious of public health pundits who loosely spread unqualified All Cause Mortality figures.

Exhibit 5 – The CDC continues to issue its pandemic summary data in the form of All Cause Mortality (ACM). As one can see in the chart above, All Cause Mortality tenders a falsely rosy depiction of the state of a pandemic (3.3%). Because of pull forward effect and the rapid decline in Covid deaths, Excess Non-Covid Natural Cause Mortality (13.3%) is a better defining statistic. All Cause Mortality only serves to conceal excess deaths during the tail of a pandemic. To date, the CDC refuses to use this important statistic in advising our governing and public health officials. You will only find it published by The Ethical Skeptic.

In the end, it is this last chart depicted in Exhibit 6 which serves to confirm the claims made in Sections 1 through 4 of this article. The level of excess natural cause death which is not Covid itself, is around 13.3% to the excess of where it should be – even given a 1.1% baseline growth inside an aging demographic (see Exhibit 6, dark orange baseline ‘annual growth’).

Exhibit 6 – Excess Non-Covid Natural Cause Mortality as a metric, serves to filter out the distractions of Covid-19 as well as mortality from accidents, overdoses, and assault – all of which serve to cloud one’s ability to observe the entailed alarming signal. As of MMWR Week 40 2022, the US has experienced an additional 385,000 natural cause deaths above and beyond what we should have seen for this period of time. Couple this with 80,000 non-natural deaths during the same timeframe, and one finds an excess of 465,000 deaths which have occurred since MMWR Week 14 of 2021. A pandemic all unto itself.

While no other public health entity appears able to or interested in tracking this critical epidemiological metric, we not only track its anomalous magnitude, but we at The Ethical Skeptic believe we know what is causing these excess deaths as well. In fact, the data by ICD-10 sub-sub-code and by US County is starkly indicative, as well as condemning. No wonder the CDC is attempting to obfuscate it – as it will serve to infuriate those whom the CDC serves. Yes, organizations of this type operate under extreme levels of conflict of interest and agency. In the immortal words of Bob Dylan,

But you’re gonna have to serve somebody, yes
You’re gonna have to serve somebody
Well, it may be the devil or it may be the Lord
But you’re gonna have to serve somebody

We choose to stand in the gap for those who cannot defend themselves. Such unequivocal inference regarding the cause of these 385,000 excess natural cause deaths will be the subject of our third article in this series, ‘Houston, We Realize the Problem (Part 3 of 3)’. A problem which is rising at 7,340 deaths per week as of 8 October 2022 – and more importantly, does not appear to be abating any time soon.

The Ethical Skeptic, “Houston, The CDC Has a Problem (Part 2 of 3)”; The Ethical Skeptic, WordPress, 24 Oct 2022; Web, https://theethicalskeptic.com/?p=68500

Torfuscation – Gaming Study Design to Effect an Outcome

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As important as the mode of inference one employs by means of a scientific study, is the design of the study itself. Before one can begin to reduce and analyze a body of observations, the ethical scientist must first select the study type and design that will afford them the greatest draw in terms of probative potential. Not all studies are equal in terms of their bootstrap nor inferential strength.
The intricacies of this process present the poseur an opportunity to game outcomes of science through study design, type and PICO vulnerabilities. Tactics which can serve to produce outcomes furthering the obfuscating, political, social or religious causes of their sponsors.

There are several ways to put on the appearance of conducting serious science, yet still effect outcomes which maintain alignment with the agency of one’s funders, sponsors, mentors or controlling authorities. Recent ethical evolution inside science, has highlighted the need for understanding that a researcher’s simply having calculated a rigorous p-value, applied an arrival distribution or bounded an estimate inside a confidence interval, does not necessarily mean that they have developed a sound basis from which to draw any quality scientific inference.1 In similar philosophy, one can develop a study – and completely mislead the scientific community as to the probative depth or nature of reality inside a given contention of science. A thousand studies bearing weak inductive inference can be rendered null by one sound deductive study. The key resides in the ethical skeptic’s ability to survey this domain of study strength and adeptly apply it to what is foisted as constituting science.

We are all familiar with the popular trick of falsely calling a ‘survey of study abstracts’, or a meta-synthesis of best evidence, or an opinion piece summarizing a body of study from one person’s point of view – a ‘meta-analysis’. An authentic meta-analysis combines congruent study designs and bodies of equivalent data, in order to improve the statistical power of the combined entailed analyses.2 The fake forms of meta-analysis achieve no such gravitas in strength. A meta-analysis is a secondary or filtered systematic review which only bears leveraged strength in the instance wherein randomized controlled trials or longitudinal studies of the same species, are able to be combined in order to derive a higher statistical power than any single study can deliver independently. Every other flavor of ‘blending of study’, fails to accomplish such an objective. This casual blending presented in the faux-flavors of meta-study may, and this is important, ironically serve to reduce the probative power of such systematic review itself. Nonetheless, you will find less-than-ethical scientists trying to push their opinion/summary articles upon the community as if they reflected through convenient misnomer, this ‘most rigorous form of study design’. One can find an example of this within the study: Taylor, Swerdfeger, Eslick; An evidence-based meta-analysis of case-control and cohort studies; Elsevier, 2014.3

This sleight-of-hand treatment stands as merely one example of the games played within the agency-influenced domains of science. With regard to manipulating study design in order to effect a desired scientific outcome, there are several means of accomplishing this feat. Most notably the following methods, which I call collectively, torfuscation (Saxon for ‘hiding the dead body in the bog’). Torfuscation is an active form of Nelsonian inference which involves one or more species of study abuse:

1. asking an orphan question, one which is non-sequitur or does not address the critical path of the scientific question at hand,

2. employing a less rigorous study type (lower rank on the Chart below) than ethically is warranted by the scientific question at hand – (aka, methodical deescalation),

3. employing an ineffective study design, and masking that error with rigorous academic statistical analysis of what is essentially garbage input,

4. selecting for a body of ‘reliable’ data to the exclusion of available and more probative data – (aka, streetlight effect), or conversely selecting for a ‘reliable’ database procedurally, which has a high probability of failure of detection or cataloging of such detection (see an example later in this article),

5. employing an ineffective secondary or filtered study design, spun as if it were a higher probative or bootstrap strength study, or

6. study constrained by a type of flawed methodical PICO-time analysis (wrong population, wrong/inequivalent timeframe between cohorts, changing context across the time period, wrong signal/indicator, etc. – see an example later in this article).

Abuses which will serve most often to weaken the probative potential of an avenue of research which could ostensibly serve to produce an outcome threatening the study sponsors. I call this broad set of pseudo-scientific practices, torfuscation.

Torfuscation

/philosophy : pseudoscience : study fraud : Saxon : ‘hide in the bog’/ : pseudoscience or obfuscation enacted through a Nelsonian knowledge masquerade of scientific protocol and study design. Inappropriate, manipulated or shallow study design crafted so as to obscure or avoid a targeted/disliked inference. A refined form of praedicate evidentia or utile absentia employed through using less rigorous or probative methods of study than are requisite under otherwise ethical science.  Exploitation of study noise generated through first level ‘big data’ or agency-influenced ‘meta-synthesis’, as the ‘evidence’ that no further or deeper study is therefore warranted – and moreover that further research of the subject entailed is now socially embargoed.

Study design which exploits the weakness potential entailed inside the PICO-time Study Design Development Model4 (see Study to Inference Strength and Risk Chart below), through the manipulation of the study

P – patient, problem or population
I – intervention, indicator
C – comparison, control or comparator
O – outcome, or
time – time series

Which seeks to compromise the outcome or conclusion in terms of the study usage; more specifically: prevention, screening, diagnostic, treatment, quality of life, compassionate use, expanded access, superiority, non-inferiority and or equivalence.

Meta-Garbage, Deescalation and PICO-time Manipulation Examples

Madsen, Hviid; A Population-Based Study of Measles, Mumps, and Rubella Vaccination and Autism, 2002.5

This first study constitutes one example of tampering with the PICO-time attributes of a research effort, wherein only medical insurance plan completed and diagnostic-cataloged records are employed (under the guise of being ‘reliable’) as the sample base for a retrospective observational cohort study’s ‘outcome’ data. Such data is highly likely to be incomplete or skewed in a non-probative or biased direction, under a condition of linear induction (a weaker form of inference) and utile absentia (a method of exclusion bias through furtive or detection-failure-laden data source selection). This study example is elicited as shown in the chart on the right, constructed from data inside the referenced Madsen study above. If the diagnosis of a condition occurs on average at 5.5 years of age inside a study population of kids, and the average slack time between diagnosis and first possible recording into a medical insurance plan database is 4 to 18 months, then a constraining of the time-series involved inside a study examining that data, to 4.5 years, is an act of incompetent or malicious study design. The study effectively screened out positive detection by inclusion criteria trickery as depicted inside the chart I developed from its time series constraints description. Only 23% of the signal population would have ever be detected, as shown in this ‘Nine Year Tracking Window’ chart. The study would rely upon a constraint which essentially bound the model to the principle that ‘only quickly-detected and visceral cases count’. This is also known as criminal activity – it is no different than cooking the books as an accountant and pocketing the cash. Except in this case hundreds of millions of innocent children and families are harmed by the entailed fraud.

Interim Estimates of Vaccine Effectiveness of BNT162b2 and mRNA-1273 COVID-19 Vaccines in Preventing SARS-CoV-2 Infection Among Health Care Personnel, First Responders, and Other Essential and Frontline Workers — Eight U.S. Locations, December 2020–March 2021.6

In similar PICO-time series manipulation, say the study team in the above second example study identifies a treatment for a disease but compares Test and Control cohorts evaluating the success of that treatment wherein the following time series inequivalents apply:

  • The Control is tracked the entire study period but the Test is only tracked for a subset thereof
  • Testing is done continuously every couple days or essentially all-day/every-day (in a symptomatic protocol), therefore the Control is exposed to detection false positives to a greater degree than is the treatment-Test cohort
  • The testing is conducted across a PICO-timeframe in which the malady is naturally in decline, an effect which serves only to benefit the Test cohort statistics because they naturally draw observations nearer to the end of the study.

To wit, all three of these advantageous constraints are exhibited in the table below, derived from data extracted from the second CDC study listed above as the second example of torfuscation. When the time series cohorts are leveled by detection arrival probability and number of study days in which the cohorts were observed, suddenly the efficacy of the treatment in question disappears (see infections per available study day). Remember, that billions upon billions of dollars, not to mention entire institutions and careers, are at stake regarding the outcome of the above study. Its result was guaranteed.

You will find both of these study-example tricks to be present in circumstances wherein a potential outcome is threatening to a study’s sponsors; political agents who hope to prove by shallow/linear inductive inference and exclusion criteria trickery that the subject can be embargoed or closed for discussion from the point of their study onward.

Moreover, a study may also be downgraded (lower on the chart below), and purposely forced to employ a lesser form of design probative strength (Levels 1 – 8 on the left side of the chart); precisely because its sponsors suspect the possibility of a valid risk they do not want broached/exposed. This is very similar to the downgrading in inference method we identified above, called methodical deescalation. Methodical deescalation is a common trick of professional pseudoscience wherein abduction is used in lieu of induction, or induction is used in lieu of deduction – when the latter (stronger) mode, type or form of inference was ethically demanded. One may also notice that studies employing these six torfuscation tricks we listed earlier are often held as proprietary in their formulation; concealed from the public or at-risk stakeholders during the critical study design phase. This lack of public accountability or input is purposeful. Such activity is akin to asking for forgiveness rather than permission, and can often constitute in reality court-defined ‘malice and oppression’ in the name of science.7

Beware of studies supporting activity which serves to place a large stakeholder group at risk,
yet seek zero input from those stakeholders as to adequacy of study design.
This is also known as oppression.

The astute reader may also notice an irony here, in that the ‘meta-analysis’ decried earlier in this article, cited the very study just mentioned as an example of torfuscation, as its ‘best evidence study’ inside its systematic review. Meta-fraud providing fraud as its recitation basis. Well, at least the species of study are congruent. If you meta-study garbage, you will produce meta-garbage as well (see Secondary Study in the Chart below).

Be very wary of a science which constrains its body of study to the bottom of
the chart below or is quick to a claim of absense (modus absens) –
especially when higher or positive forms of study are available
but scientists are dis-incentivized to pursue them.

Study Design to Mode of Inference Strength and Risk

The following is The Ethical Skeptic’s chart indexing study design against mode of inference, strength and risk in torfuscation. It is a handy tool for helping spot torfuscation such as is employed in the three example types elicited above (and more). The study types are ranked from top to bottom in terms of Level in probative strength (1 – 8), and as well are arranged into Direct, Analytical and Descriptive study groupings by color. Torfuscation involves the selection of a study type with a probative power lower down on the chart, when a higher probative level of study was available and/or ethically warranted; as well as in tampering with the PICO-time risk elements (right side of chart under the yellow header) characteristic of each study type so as to weaken its overall ability to indicate a potential disliked outcome.

The Chart is followed up by a series of definitions for each study type listed. The myriad sources for this compiled set of industry material are listed at the end of this article – however, it should be noted that the sources cited did not agree with each other on the material/level, structure nor definitions of various study designs. Therefore modifications and selections were made as to the attributes of study, which allowed for the entire set of alternatives/definitions to come into synchrony with each other – or fit like a puzzle with minimal overlap and confusion. So you will not find 100% of this chart replicated inside any single resource or textbook. (note: My past lab experience has been mostly in non-randomized controlled factorial trial study – whose probative successes were fed into a predictive model, then confirmed by single mechanistic lab tests. I found this approach to be highly effective in my past professional work. But that lab protocol may not apply to other types of study challenge and could be misleading if applied as a panacea. Hence the need for the chart below.)

Study Design Type Definitions

PRIMARY/DIRECT STUDY

Experimental– A study which involves a direct physical test of the material or principal question being asked.

Mechanistic/Lab – A direct study which examines a physical attribute or mechanism inside a controlled closed environment, influencing a single input variable, while observing a single output variable – both related to that attribute or mechanism.

Controlled Trial

Randomized (Randomized Controlled Trial) – A study in which people are allocated at random (by chance alone) to receive one of several clinical interventions. One of these interventions is the standard of comparison or the ‘control’. The control may be a standard practice, a placebo (“sugar pill”), or no intervention at all.

Non-Randomized Controlled Trial – A study in which people are allocated by a discriminating factor (not bias), to receive one of several clinical interventions. One of these interventions is the standard of comparison or the ‘control’. The control may be a standard practice, a placebo (“sugar pill”), or no intervention at all.

Parallel – A type of controlled trial where two groups of treatments, A and B, are given so that one group receives only A while another group receives only B. Other names for this type of study include “between patient” and “non-crossover” studies.

Crossover – A longitudinal direct study in which subjects receive a sequence of different treatments (or exposures). In a randomized controlled trial with repeated measures design, the same measures are collected multiple times for each subject. A crossover trial has a repeated measures design in which each patient is assigned to a sequence of two or more treatments, of which one may either be a standard treatment or a placebo. Nearly all crossover controlled trial studies are designed to have balance, whereby all subjects receive the same number of treatments and participate for the same number of periods. In most crossover trials each subject receives all treatments, in a random order.

Factorial – A factorial study is an experiment whose design consists of two or more factors, each with discrete possible values or ‘levels’, and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be called a fully-crossed design. Such an experiment allows the investigator to study the effect of each factor on the response variable or outcome, as well as the effects of interactions between factors on the response variable or outcome.

Blind Trial – A trial or experiment in which information about the test is masked (kept) from the participant (single blind) and/or the test administerer (double blind), to reduce or eliminate bias, until after a trial outcome is known.

Open Trial – A type of non-randomized controlled trial in which both the researchers and participants know which treatment is being administered.

Placebo-Control Trial – A study which blindly and randomly allocates similar patients to a control group that receives a placebo and an experimental test group. Therein investigators can ensure that any possible placebo effect will be minimized in the final statistical analysis.

Interventional (Before and After/Interrupted Time Series/Historical Control) – A study in which observations are made before and after the implementation of an intervention, both in a group that receives the intervention and in a control group that does not. A study that uses observations at multiple time points before and after an intervention (the ‘interruption’). The design attempts to detect whether the intervention has had an effect significantly greater than any underlying trend over time.

Adaptive Clinical Trial – A controlled trial that evaluates a medical device or treatment by observing participant outcomes (and possibly other measures, such as side-effects) along a prescribed schedule, and modifying parameters of the trial protocol in accord with those observations. The adaptation process generally continues throughout the trial, as prescribed in the trial protocol. Modifications may include dosage, sample size, drug undergoing trial, patient selection criteria or treatment mix. In some cases, trials have become an ongoing process that regularly adds and drops therapies and patient groups as more information is gained. Importantly, the trial protocol is set before the trial begins; the protocol pre-specifies the adaptation schedule and processes. 

Observational – Analytical

Cohort/Panel (Longitudinal) – A study in which a defined group of people (the cohort – a group of people who share a defining characteristic, typically those who experienced a common event in a selected period) is followed over time, to examine associations between different interventions received and subsequent outcomes.  

Prospective – A cohort study which recruits participants before any intervention and follows them into the future.

Retrospective – A cohort study which identifies subjects from past records describing the interventions received and follows them from the time of those records.

Time-Series – A cohort study which identifies subjects from a particular segment in time following an intervention (which may have also occurred in a time series) and follows them during only the duration of that time segment. Relies upon robust intervention and subject tracking databases. For example, comparing lung health to pollution during a segment in time.

Cross-Sectional/Transverse/Prevalence – A study that collects information on interventions (past or present) and current health outcomes, i.e. restricted to health states, for a group of people at a particular point in time, to examine associations between the outcomes and exposure to interventions.

Case-Control – A study that compares people with a specific outcome of interest (‘cases’) with people from the same source population but without that outcome (‘controls’), to examine the association between the outcome and prior exposure (e.g. having an intervention). This design is particularly useful when the outcome is rare.

Nested Case-Control – A study wherein cases of a health outcome that occur in a defined cohort are identified and, for each, a specified number of matched controls is selected from among those in the cohort who have not developed the health outcome by the time of occurrence in the case. For many research questions, the nested case-control design potentially offers impressive reductions in costs and efforts of data collection and analysis compared with the full case-control or cohort approach, with relatively minor loss in statistical efficiency.

Community Survey – An observational study wherein a targeted cohort or panel is given a set of questions regarding both interventions and observed outcomes over the life or a defined time period of the person, child or other close family member. These are often conducted in conjunction with another disciplined polling process (such as a census or general medical plan survey) so as to reduce statistical design bias or error.

Ecological (Correlational) – A study of risk-modifying factors on health or other outcomes based on populations defined either geographically or temporally. Both risk-modifying factors and outcomes are averaged or are linear regressed for the populations in each geographical or temporal unit and then compared using standard statistical methods.

Observational – Descriptive

Population – A study of a group of individuals taken from the general population who share a common characteristic, such as age, sex, or health condition. This group may be studied for different reasons, such as their response to a drug or risk of getting a disease. 

Case Series – Observations are made on a series of specific individuals, usually all receiving the same intervention, before and after an intervention but with no control group.

Case Report – Observation is made on a specific individual, receiving an intervention, before and after an intervention but with no control group/person other than the general population.

SECONDARY/FILTERED STUDY

Systematic Review/Objective Meta-Analysis – A method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power. This conclusion is statistically stronger than the analysis of any single study, due to increased numbers of subjects, greater diversity among subjects, or accumulated effects and results. However, researchers must ensure that the quantitative and study design attributes of the contained studies all match, in order to retain and enhance the statistical power entailed. Mixing lesser rigorous or incongruent studies with more rigorous studies will only result in a meta-analysis which bears the statistical power of only a portion of the studies, or of the least rigorous study type contained, in decreasing order along the following general types of study:

Controlled Trial/Mechanism
Longitudinal/Cohort
Cross-Sectional
Case-Control
Survey/Ecological
Descriptive

Interpretive/Abstract ‘Meta-Synthesis’ – A study which surveys the conclusion or abstract of a pool of studies in order to determine the study authors’ conclusions along a particular line of conjecture or deliberation. This may include a priori conclusions or author preferences disclosed inside the abstract of each study, which were not necessarily derived as an outcome of the study itself. This study may tally a ‘best evidence’ subset of studies within the overall survey group, which stand as superior in their representation of the conclusion, methodology undertaken or breadth in addressing the issue at hand.

Editorial/Expert Opinion – A summary article generally citing both scientific outcomes and opinion, issued by an expert within a given field, currently active and engaged in research inside that field. The article may or may not refer to specific examples of studies, which support an opinion that a consilience of evidence points in a given direction regarding an issue of deliberation. The author will typically delineate a circumstance of study outcome, consilience or consensus as separate from their personal professional opinion.

Critical Review/Skeptic Opinion – A self-identified skeptic or science enthusiast, applies a priori thinking with no ex ante accountability, in order to arrive at a conclusion. The reviewer may or may not cite a couple examples or studies to back their conclusion.

Sources: 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

The Ethical Skeptic, “Torfuscation – Gaming Study Design to Effect an Outcome”; The Ethical Skeptic, WordPress, 15 Apr 2019; Web, https://wp.me/p17q0e-9yQ

An Official ‘Thank You’ to Science Based Medicine

Posted on by
Rookem’s Razor:  All things being equal the most expensive explanation tends to be the correct one. Chronic and severe pain is a no bullshit tolerance factor, it serves to make one a skeptic, very fast.
WARNING: The following testimonial constitutes an ‘anecdote’ and cites a resolution based upon actual patient input to doctors. When you have gotten up off the floor after fainting from the horrific pseudoscience of it all, simply click your heels three times and repeat over and over “The plural of anecdote is not data” and it will be all better.

I wasted three years of suffering and then $16,300 pursuing the approach Science Based Medicine might prefer for a malady which my Integrative Medicine Practitioner resolved in one appointment, $200 and by means of a supplement. Which now is quackery indeed? I am now in doubt. Patient success experience or revenue goal oriented scripted shotgun testing? Well let’s hold on that conclusion. This was not the last time unfortunately I was to have been harmed by bad medical advice coming from a Science Based Medicine representative ‘skeptic’.

One thing that I do know is that, were I to practice the Big Healthcare method on the left (chart at bottom) in my labs, I would be committing lab fraud in order to bilk my clients out of artificially prolonged revenue through fake science (see The Lyin’tific Method). In Science Based Medicine however, fraud only applies to outsiders, and is misdefined as anything that does not revenue-serve their cronies, any competing business or anything else they decide that they do not like.

Rest assured, Science Based Medicine is not promoting their view of medical care as a smart option for your consideration. Their heading is one of supporting the elimination of your choices and control over your own health, independent of the secure profit pathways for big pharma, big healthcare and their oligarchy cronies extracting from both.

The empirical results are proving to be abysmal, both in terms of the cost of healthcare and the overall health and well being of Americans.

This enormous harm and suffering caused by fake skeptics is just one key reason why skeptics are losing the battle for the American mind.

The Odyssey

rookems razorIt is a scam, and a crime of fraud, that I could not have been instructed about this supplement two decades ago. Why? Because hinting that a supplement might help my flank pain might ‘constitute claims of cures or treatments.’ This is how a mafia works. As a result of our sounds-good-on-paper ‘science’ millions of Americans suffer needlessly, and moreover are being held hostage and bilked out of tax and critical household budget dollars to the tune of $23,000 a year and dramatically upwards, to fund a scam in socialist medicine. Let’s examine how this fraud, activist-supported by Social Skepticism, works.

Yes, indeed I wanted to send out a heartfelt ‘thank you’ to the Science Based Medicine crowd today.  Before I do that however, let me relate the Odyssey the science gods tasked me with in my search to resolve increasingly severe lower left quadrant pain. You see over the last two years I have been experiencing recurrent lower left quadrant pain, commensurate with a whole host of other partially debilitating symptoms; none the least of which included facial sores, dizziness, ear ringing, anxiety, hair color dimming, weakness in my workouts, cold sweats, mental fog. I speak often as part of my living in support of my businesses and those subjects inside which I carry a passion. Anything which negatively affects my pneumogastric nerve and central nervous system is an unwelcome life contributor. Of course this greatly distressed me so I went, in 2013, to my General Practitioner with my complaint. He immediately scheduled me for a battery of expensive tests, starting with a colonoscopy which cost my medical plan $6,500, and then proceeded to progressively more and more likely candidates of diagnosis. You will find in social skepticism that the precautionary principle, only applies to the opportunity to make medical profits – aside from that it is always pseudoscience. We followed the Big Healthcare, Big Pharma, Big Skepticism script, much of which agrees with the SBM doctrine. You are stupid, integrative medical professionals are evil, doctors have your best interests as first priority (note: ‘best interests’ correlate to profits with an Pearson r = .99).1

Well sadly, 26 weeks and more later, we found nothing, and nothing worked. Quietly I was told by my Gastroenterologist, “I used to get 3 patients a week with unresolved flank pain back years ago. Now I get 8 patients or more per day. It is my number one patient challenge.” This is called Intelligence, inside The Real Scientific Method – but it is the kind of thing which fake skeptics dismiss with a wave of the ‘it’s nothing but a change in awareness’ hand. And they repeatedly enforce this claim upon science, media and public before any evidence has arrived for the most part – see The Art of the Professional Lie: Einfach Mechanism. Fake skeptics know well that an idea is hard to unseat once it has been uttered as truth, by Lindy Effect alone. You win, absent of much data or study at all. Just scream the conclusion as early as is possible, or after you see any hint of inductive support for your a priori notion. Never mind that everyone else in the world can spot your bias and predict exactly what your conclusion will be, never mind your eroding credibility, just do it. This is just one of the ways in which these fakers outsmart themselves, unjustifiably influence society and cause us all harm and suffering.

‘The plural of anecdote is not data.’

Take it from a successful intelligence professional and research lab head, never trust a person who utters this phrase. They have never accomplished even the first step of real science. They are a diagnostician at best.

At the conclusion of 30 weeks of expensive investigation I was told that I “must have Irritable Bowel Syndrome (IBS).” This is the pandemic set of symptoms from which an alarmingly increasing number of people suffer; one in which we have no idea what even causes it. Some pretty smart citizens have some well backed ideas, but of course they are slapped down as evil by SBM ‘skeptics’. I am told that the common occurrence of my pain simply stems from ‘an increase in awareness’ as well, for we have to remember that when Science Based Medicine puts out a claim, it does not have to be justified – simply promulgated to the masses unquestioned (see Appeal to Skepticism Fallacy). Now, forget the 54 million suffering people; …fuck them, our science super hero skeptic skills are needed for another much more pressing issue, Homeopathy Awareness! …ehh, that and some bigfoot scrutiny every now and again. Issues of focus that hallmark an ethical and humanity serving life.2 3

…please hold on a moment while I contain my laughing.

I had run into this ilk of malicious and oppressive incompetence before with the stupid idiot at Science Based Medicine who assured me that he represented science, and that folic acid and methylfolate were the same thing, and finally that I was an idiot for thinking any different. This professional medical advice (quackery) served to harm me greatly – as it turned out that I was suffering from decades of macrocytic anemia from having taken folic acid, instead of folate. Fortunately, another thing which was corrected by patients working with patients – and not by appeal to authority as science based medicine.

Irritable Bowel Syndrome (IBS) of course is the diagnosis of last resort after all the money avenues have been exhausted. Now I must learn to live with my IBS, because there is no cure. There is obviously ONE contributing element since the skyrocketing started as a discrete event shortly after 1994 – but I can assure you there is no one cure.  Or, it could simply be the ‘luck of the draw’ genetically. You won the lottery again – funny how that keeps happening with medical stuff and never seems to happen at the MiniMart Lotto counter. Nothing in the environment causes it, certainly not glyphosate, and sadly nothing you can do about curing it. Your pain, slight fever, dizziness, and mental fog are all from panic attacks! Yes that is it, our go-to conclusive pseudo-hypothesis is one which cannot be tested for falsification! Such brilliance! Plenty of inductive study to confirm it. Proliferating as so many psychology studies do – standing as imperative proof that you are just weird – we are done! Yay for Science!

I was told to start to meditate and maintain an ‘awareness’ of your ‘triggers’ and ‘stress related episodes’ – as a good portion of this is psychological. Really your fault anyway, for worrying too much. But I do have an expensive set of prescriptions which can alleviate symptoms in the mean time. A funny thought drifted through my head, I had heard this line before – dermatologists attempting to treat my rosacea with $400 a month topical creams, each one of which never seemed to work; and upon being unsuccessful, fell back on this same failed message:

‘Triggers’, the watchword of the lazy and incompetent
‘Stress’, the ad hoc fallacy theory of the century
‘Awareness’, the buzzword of the incompetent and malicious.

Yes, I had heard this line of ‘go away, it’s all your fault’ sales bullshit before. 2018 note.4

Cut down on sugar, coffee, fast food and soft drinks, and exercise more. Yep, heard that too. Oh? You run 4 miles a day now and don’t do any of these things?  Well, meditate more and take these two new prescriptions.  An antispasmodic for your pain, and a relaxer to stop your ‘panic attacks’ and over-worrying. These two are a mere $92 a month more on top of your other medical costs.  A drop in the bucket really.

I Had the Ruby Slippers All Along – Finally Success!

This year, when my IBS started flaring up again, I went to my Integrative Medicine Practitioner this time. She said: “This sounds an awful lot like stomach HCL insufficiency and dysbiosis. I get a lot of this lately, let me tell you. In addition, you had h. pylori after one of your Far East projects, and it can wreak havoc in your stomach’s acid production. Your digestive system is possibly not putting out enough hydrochloric acid and enzyme base. As a result your undigested food is putrefying5 in your small intestine and causing a whole host of dysbiosis symptoms, (my spell checker does not even recognize the words ‘dysbiosis’ or ‘SIBO’ despite its presence as a pandemic in the US population – see chart below) auto-immune, food-sensitivity reactions and pain from that point on in your gastrointestinal tract. Let’s try this first and see how it works: Get an HCL/Betaine/Enzyme supplement and make sure you take 1000 – 2000 mg with each meal.”

Well, they say that science is probative based upon incremental risk. My integrative medical practitioner had made an incremental conjecture of risk – and I was about to test it.  And chronic pain is a NO BULLSHIT tolerance factor – it makes you a skeptic fast. She had put her conjecture on the line of risk. As a skeptic, I at least admired that.

Oh my GAWD, my Holistic/Integrative Practitioner recommended a supplement! Oh the quackery!! Let the FDA Letters fly! People are dying! Big Pharma Skeptics rescue us! Let’s take just a second to examine what is going on regarding this grand odyssey of which I only suffered in microcosm. Unresolved flank pain (IBS) is skyrocketing right now in the United States. Any self respecting skeptic should be highly energized to look into such a stark signal. In contrast, aside from accidental overdoses, extreme body builders and reckless weight loss practices, no one is getting hurt by supplements.

The reality is we needed real skeptics back in 1991-1994 when our food base was planned to be changed dramatically based upon paltry science – unfortunately all we got was this cadre of goofball science pretenders who betrayed our consumer health base. They were all caught up in Bigfoot, UFO’s and the Loch Ness Monster back then too. Unfortunately they missed this important issue demanding skepticism, in the mean time. That is why I am speaking up.

When Social Skepticism ignores skyrocketing public health and human rights concerns such as SIBO, and the proliferating list of auto-immune generated maladies stemming from our food, in favor of chasing supplements on behalf of Big Pharma regulation – this activity is not simply pseudo-science or social epistemology, it is called more specifically a legal term: malice and oppression.

Wow, after three full years of misery and health degradation, problem solved. All symptoms are abated and even my hair color is growing robust again.  But I am so much more educated you know?  And I have Science Based Medicine to thank for that education. I made a decision in accordance with their philosophy which obtained for me a $16,300 education (that because of skyrocketing MooP/deductible costs, on top of the $23,000 we all pay each year already for the health scam). This misadventure alone tallied to 1/5th of the cost of my graduate degree! So I had to learn something, right?! Otherwise I would have never experienced the educational menagerie of things you can do, when you have lots of tools, billable pathways and rote formulas at hand, but don’t have a clue what you are doing.

2019 Update:  My healthy family plan medical cost per month is now $2,767. If I held a regular job, like I did in 1998 – I would be losing my home right now from the disease of being healthy and working in America.

This is criminal. Science Based Medicine this reality of citizen suffering is in part, YOUR fault – because healthcare plans have to pay for your promoted ‘science based’ practices and the large scale fraud/exploitation of hard working families you defend.

Were I to practice the method on the left of the 2013 to 2014 comparative chart (above right) in my labs, I would be committing lab fraud in order to bilk my clients out of artificially prolonged revenue through fake science. Yes, I own and have owned several science research and application development labs through the years.  In those operations, I could never ethically perform the list on the left under REAL science.

Oh, and one small note:  If you have an entry in your records, even one instance that you were referred to one of the specialists on the left in the chart above, and you DID NOT follow the doc’s referral, then you will be declined for health insurance coverage regarding that issue when you apply later in life – say 20 years later – even if you are fine now.  I know this from experience having managed several company health insurance plans for my employees.  The Health and Pharma industry just cannot seem to get your history of records readily available to the doctors for your health needs, like my doctor could not look up the fact that I had h. pylori in my history, that might have saved me lots of money in this case.  But the industry can sure make a DETAILED history of your health decisions readily available for the insurance companies – off of which to identify a non-compliant action on your part, in order to extort money from you.

Such is the generous, self promoting gift of Science Based Medicine. One that keeps on giving in terms of destroying households financially, and in terms of personal health. An elegant accomplishment, harming citizens in two ways at once. Fighting for the rights of oligopolies and cartels everywhere. Well done. So sciencey.

epoché vanguards gnosis

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How to MLA cite this blog post =>

The Ethical Skeptic, “An Official ‘Thank You’ to Science Based Medicine” The Ethical Skeptic, WordPress, 9 Nov 2014; Web, https://wp.me/p17q0e-3gy