I've Spent At Least A Decade In The Vaccines Harms Debate

Let me show you some of their tricks

Discussing with commentator Kalle Pihlajasaari on the Rebuttal To Rootclaim article about serious flaws in a peer reviewed paper I examined, I realised knowledge on common flaws with vaccine papers is not widespread.

I doubt even within the scientific community it is known. This is based on observation and experience.

1. Lumping All Vaccines Together

Quickest way to destroy a risk signal in a vaccine is to lump everything together.

Everyone understands averaging. I have a high number - 120. And other, low numbers. 19, 14, 17, 22, 5. Imagine they represent deaths, or injuries. Individually, you clearly see 120 is the worst, and the 5 is the ‘best’.

Lumping together - 197 total. Divide by how many numbers there are - 6 - you get 32.8! Wow, seems so low! Notice we’ve lost the risk signal of 120 deaths, and submerged the better signal of 5?

Vaccines - even for the same disease - have different ingredients, different manufacturers, different processes. Bad peer review vaccine safety papers lump vaccines together.

Observe the FDA talking about common ingredients in vaccines, lumping billion+ worth of doses together.

By focusing on “common” ingredients, they distract you from distinct and unique ones, making all vaccines seem equal and interchangeable. They’re not. It is an awful comparison for safety.

2. Using A Non-Placebo Placebo

Placebo, a chemical that doesn’t do anything. Often, if testing a new drug, it is compared to placebo (usually sugar for oral (mouth), saline (salt water) for injectibles). If it performs worse than, or ‘equally as well as’ the placebo, the drug is flawed.

Pharmaceuticals will replace “placebo” with a non-placebo (something with active ingredients), and falsely call it placebo. For example, comparing their vaccine to another vaccine. It isn’t placebo because vaccines contains active ingredients. If they didn’t… they wouldn’t work.

Pharmaceutical shills write misleading hitpieces defending this unsound practice, invoking vague references to ‘ethics’. Misusing the term placebo is highly unethical. Their “ethics” relies on a circular reasoning fallacy, assuming shots are already proven safe - despite safety being based on dubious practices.

Saline is 100% safe, so zero reason to object, unless you’re engaging in underhanded research.

3. Using Broken Prior Approvals To Justify The ‘Safety’ On Another Shot

Regulatory capture gets an unsafe product through - via non-peer reviewed authorisation - then they use that to set the baseline for ‘safety’ in future studies on new shots in journals.

Imagine: FDA approves insta-death cynanide shot after everyone from the board of pharmaceutical cyanide manufacturers ends up working at the FDA. Cynanide manufacturer doesn’t need to keep meeting the ‘old’ safety standard, they compare their cynanide mark II shot to the previously approved mark I shot. Perfect Trojan horse.

Numerous papers use this trick, saying the ‘new’ shot is ‘no worse’ than the old shot therefore it is ‘safe’. FDA aren’t a formal peer-review process, they’re a political agency, with a head chosen by politics. Not fit for purpose.

Pharmaceutical CEOs become head of the FDA. What they approve politically, is irrelevant and non-applicable from a safety testing standpoint.

Even if Cynanide Inc don’t get their dodgy shot through, Poison Ivy LLC only need to get theirs in, and Cynanide Inc can compare theirs to Poison Ivy LLC’s shot. One bad approved shot lowers the safety bar for everybody.

4. Claiming ‘Ethics’ To Justify Engaging In Bad Study Practices

When caught - per McShill hitpiece above - vaccine “researchers” and manufacturers backpedal, insist they can’t compare vaccinated to unvaccinated, saying it is ‘unethical’ to withhold shots from the unvaccinated.

Fallacy. Circularly assumes safety data exists to justify giving the shots is ‘ethical’ to begin with - which it doesn’t: they admit they’ve never compared it to the unvaccinated. No baseline safety data verifying the ‘ethics’ exists. An untested assumption. Evil.

Refering to endless papers that exhibit one or more of the flaws and tricks described here, as ‘justification’ for the ‘ethics’, despite being unethical. Irony abounds.

Isn’t it ethical to have accurate safety reporting than rushing out products at profit? If truly concerned about ethics - it’d be done at cost, taking their time.

5. Cherry Picking Adverse Outcomes Data

They will search far and wide for benefits - all cause mortality, hospital stays, number of sick days from work, scraping the barrel, talking about the number of doctors appointments. Reducing appointments is always good - especially if the person is dead, right?

On adverse outcomes, they cherry pick. Focus on one disability, narrow time range. They’ll apply arbitrary (unjustified) age ranges (‘we excluded those over the age of 6’), date ranges (‘we only included those who got their shot prior to…’), engage in weird selections (‘those who got their shot after age 6 were considered unvaccinated’).

Imagine a car safety study only studying the number of tyre blowouts during a particularly dry summer (with non-adverse weather), ignoring the number of steering malfunctions a car experienced. Terrible.

6. Using A High ‘Benefit’ To Justify High Harms

When risk signal data is overwhelming, ‘peer-reviewed’ papers backpedal. How often have you heard: ‘the benefits outweigh the risks’ or ‘the harms don’t outweigh the benefits’? What s**tty logical reasoning.

Rather than admit fault, recall, redesign and improving it, they instead barrel scrape on perceived (not actual) benefits, retroactively justifying the risks that their original peer review missed.

Imagine an ambulance has a 1 in 10,000 chance of exploding every trip, but ambulances save so many lives, so a paper argues to keep using them. Benefits outweigh risks.

Saying benefits outweigh risks does not excuse the pharmaceutical companies from improving the safety of their product. It is pure and utter evil, costing people’s lives.

7. Stratifying The Target Audience

They hide risk data by stratifying by recipient. Unsafe in children? Pharmaceutical companies scream it is great for the elderly.

Regulatory capture buddies, FDA, MHRA, HealthCanada, EMA, whichever crooked mass murdering organisation lacking working guilty consciences for all murders, rubberstamp for a different target audience, based on the stratified dataset.

So “benefits” for the elderly, by magic of corruption becomes “benefits” for children, even when the safety data shows otherwise.

8. Downplaying/Minimising/Gaslighting The Risks

Risks are shown to be terrible in all audience categories, no matter who gets it - whether they be dying from leprosy or as fit as a fiddle - it is bad for all.

Pharmaceutical companies publish ‘peer reviewed’ papers that gaslight people, saying ‘1 in 20,000 is a ridiculously low percentage’, followed by some wrong percentage done by number fudging, like 0.0000423%, often lumping vaccines, adding other demographs, etc.

Looks embarassing and absurd, doesn’t it?

If I murder 1 in every 20,000 people I meet, I’d go to jail as a serial killer, but if a pharmaceutical CEO sells a ‘product’ that kills 1 in every 20,000 it meets, that’s an ‘acceptable risk’.

These cases represent a 1 in 20.000 risk for Norwegians of experiencing the adverse reaction.

Imagine if Ted Bundy was declared safe because he ‘only’ killed 36 out of 230 million Americans, meaning he had a low 0.0000156% chance of killing someone. Ted Bundy is safer than the AstraZeneca shot.

This pure fabrication and gaslighting, they’re parroting unsubstantiated ‘safety’ claims unchallenged. Studies neglect the huge underreporting (Lazarus report says less than 1% get reported), failing to admit that products can be recalled and improved.

Just accept the slop you’re given peon. Don’t you know daddy pharmaceutical with their billions worked hard to give you this low quality clot-inducing shot?

9. Doing Very Shallow Meta-Studies

Meta-study - supposedly the ‘golden standard’ - is supposed to be a peer reviewed study of other peer reviewed studies. Like this weird, peer review pyramid, the top is supposed to be better supported than the bottom.

More a house of cards done wrong. Rather than discuss studies in-depth, a bad meta-study will summarise bad studies (exploiting tricks and flaws), then appeal to quantity, instead of quality.

Example: they treat a single case study as equal to a case study of thousands. Flawed. The quality of peer review papers wildly varies. Shouldn’t be an exercise in counting.

Red flag if it says ‘most of the studies show that…’, without detail. They could be the worst studies going. ‘Just trust us bro’. Obfuscating narrative control attempt, saying ‘no need to look, I checked, it says our safety is good, see, I counted them’.

One example: Johns Hopkins abstract claiming SV40 contamination - a serious safety failing - is okay, because study count go high; studies hidden behind paywalls. Emphasis added:

The present review of recent studies showed that the earlier results describing the recovery of SV40 DNA sequences from a large proportion of the above tumors were not reproducible and that most studies were negative.

Look! Most negative! 51 out of 100 is a win, right? Quantity trumps quality. Ignore the positives, don’t count! Fail to explain why there were positive findings. Ignore, majority consensus rules!

10. Lumping Unvaccinated And Vaccinated Together

Anyone with less than X number of arbitrarily selected doses counts as being ’not fully vaccinated’. Says ‘we’re lumping unvaccinated and vaccinated together selectively fudging the datasets’. Evil.

This trick lumps injured with uninjured into an obscuring pile. Like lumping vaccine makes and models. So even when they have unvaccinated people to use for study data - they still don’t.

11. Intentionally Watering Down Recorded Data

US’ VAERS, UK’s YellowCard reporting scheme, etc, all water down recorded data on adverse signals. There’s no standardisation. Doctors can write whatever they want.

The UK’s system is even worse, with injuries tallied into a Spreadsheet as if compiled by Joseph Stalin himself. One death is a tragedy, a million injuries is just a YellowCard reporting scheme statistic.

By watering down the recorded data, they make it intentionally impossible to retroactively study the dataset to find risk signals in vaccines.

12. Skip Retroactive Investigations Entirely

Even when it does get finally acknowledged there are issues - like with the narcolepsy causing Swine Flu shot in the UK back in 2009 - there will be zero follow up investigation to determine what caused the injuries. None at all. Swept under the carpet.

Despite being a prime example of a defective product, an opportunity to learn from mistakes, no attempt to ensure it doesn’t happen again by peer review examinations is performed. People must be sacrificed to ensure the reputation of the vaccine must remain unquestioned.

The manufacturers are clearly at fault, the product is defective, but it is treated like a dart board to blindly throw shots at, rather than a retrospective analysis to find the cause. It is almost like the scientific community don’t want to ask these difficult questions.

13. Victim Blaming

An overused classic. So instead of blaming the faulty shot, some peer review papers will blame the victim. A classic is the suggestion the shots ‘react’ with people who have ‘certain genes’, or that it ‘only affects certain people’, as if they’re the odd ones out, and only normal people have no reactions.

A non-faulty product wouldn’t cause issues in “certain people”. Imagine suggesting a seatbelt doesn’t work correctly if you have the wrong genes.

14. Source Conflation

Another grand trick is to suggest multiple sources for an issue. For example, when it comes to the disease GBS, papers will mention that not only do some vaccines cause it - but also some viruses do. As if that makes the vaccine causing it somehow acceptable.

It’s a bit like saying some serial killers kill people, but then again, so do some cars, so serial killers aren’t that bad really. Yet more terrible logic.

15. The ‘We Couldn’t Be Bothered To Look’ Study

Another classic, where a study basically says ‘we couldn’t work out how it could work, we couldn’t find anything, other people couldn’t be bothered to find anything, so harms must not exist’. Sounds absurd? Here’s one example:

Currently, no mechanisms have been demonstrated that could explain the correlation between vaccination and the development of autoimmune diseases. Furthermore, epidemiological studies do not support the hypothesis that vaccines cause systemic autoimmune diseases.

Remember, safety has to be proven, not harms.

It doesn’t say what studies, it doesn’t go into detail on why they don’t support it. It isn’t necessary for a cause or a mechanism to be proven for a trend to exist. If I say people prefer to sleep at night, and someone screams I haven’t shown a cause for it, it doesn’t mean the trend doesn’t exist.

Notice it does not say ‘we found evidence showing the safety of the shots’, but ‘we could not find evidence of harms’. They’re two different standards. Why would a pharmaceutical company be motivated to find harms? They’re already shown to be selfish and evil committing mass fraud.

16. Ad Hominem Attacks

When pharmaceutical shills and their stooges get really desperate - exposed, even - they attempt to attack the credibility of the critic, and either destroy their credibility (as did Brian Deer who tried to slander many vaccine injury victims for profit without disclosing his conflicts of interest), or attempt to find some irrelevancy about a person’s history - such as saying ‘they sell books for profit!’ or ‘they ask for money!’.

Ignore the fact phamaceutical companies commit 2.3 billion in fraud on the regular, make billions of dollars worth of profit, lobby the s**t out of government, buy out large numbers of ads and influence the entire media and regulatory industry: no, focus on the guy making a few thousand on books discussing this issue.

17. File Malicious Complaints, Takedown Notices, Engage In Underhanded Behaviour

When even credibility attacks fail, pharmaceutical companies and their regulatory capture ilk will attempt to censor critical peer review, undermine, retract (without justification), alter, edit, or even remove tables and datasets to ensure any critical information is kept out of the information sphere. FDA demanding 75 years to release information, for example.

Deplatforming, censorship, avoiding discussions, bypassing advisory committees and challengers, are all part of this fraudulent deception campaign.

These pharmaceutical shills are truly disgusting, the lowest of the low, and they will stop at nothing to deceive you into getting their dangerous and faulty products. No-one with a functional product would have to stoop to such BS tactics.

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