I have been wondering for a while how much "counting the vaxxed as those 14 days past the jab" is hurting the unvaxxed numbers. This article is shocking. It's grievous how much Pharma and govt agencies are withholding and twisting information.

I don't usually post but thank you El Gato Malo for writing all of your articles. You have done so much work and there are many of us who greatly appreciate it. And I like the cats pics. ;)

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They are profit boosters with negative medical benefit.

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Mar 21, 2022·edited Mar 21, 2022

The only logical reason for defining an "early post-booster" category of day 3-7 is to conceal the effects on their numbers of the entire post-booster period. Don't we know, for instance, that the worst adverse effects happen in the first 48 hours post vaccination? In doing so, they can dismiss analyses like yours that are forced to make assumptions based on their purposefully incomplete data. They know few are going to see your analysis let along attempt to follow your logic. Pure obfuscation at its worst.

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Good work. Rigging studies is of course de rigueur and has been going on decades, which is why we are at the edge of the failure of so many systems that we’re on the verge of either a revolution or total destruction. But of course that is the story of central authority for 10k years. Hopefully we can do it peacefully. Sometimes the fraud is methodological, sometimes analytical. In my own particular line of work, it generally comes under methodology. e.g. "Let's assess the effectiveness of a fever vaccine by letting people in the trial self-dose with anti-pyretics to suppress their fever." haha

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gato, you're wonderful, but I think there is a big mistake here.


a = number of infections in booster group

b = number of person-days in booster group

c = number of infections in early postbooster group

d = number of person-days in early postbooster group

I think to properly combine the risks from the "early postbooster" and "boosted" groups, you need to do (a + c) / (b + d), whereas what you've done is a / b + c / d.

As a toy example to demonstrate this, imagine the booster were just a placebo, and neither increased nor decreased your chance of being infected. Then the rate of infection in each of the three groups (unboosted, early postbooster, booster) would be exactly the same number. Let's say, 30,000 infections per 100,000 population per year. (This is because the paper divides by the number of person-days at risk instead of merely the population.)

I think what you're doing is tantamount to combining the 30,000 infections per 100k per year from the boosted group and the 30,000 infections per 100k per year from the early postbooster group and concluding that the booster doubles your infection rate per year to 60,000 per 100k, when in reality it merely did nothing at all.

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Gato, you have a gross mathematical error in your calculations above.

Yes, the absolute number of infections in the first 12 days is probably ~2.4 times higher than the number given in the day 3-7 category. However, so is the number of person years.

As soon as you convert to the relative measure, you can no longer argue about hidden risk in the early booster group.

2,500 infections in 5 million person days lead to the same rate as 6,000 infections in 12 million person days. The rate stays the same.

Even worse, you seem to have applied the correction factor of ~2.4 to the sum of the relative risk (boosted + early boosted). So not only are you correcting an already corrected (relative) number, you are even correcting the part of that number that doesn't need correction - the boosted group.

As much as I agree with you on the point of data manipulation by definition of vaxxed/boosted, this post is simply teeming with errors and I'm afraid the majority of your readers will lack the mathematical/statistical prowess to realise it.

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Hate to be bearer of bad news but the initial analyses is flawed. You cannot add the per 100k/yr data in this way. When you divided the infections by person years the data was already normalized for time. Changing from 5 days to 12 days adjusts both denominator and numerator by the same factor which divides out. Instead what should been done is to multiple the infections and the days at risk by the 12/5 factor. Then add these amounts to the booster days at risk and infections. Then calculated the adjusted infections for 100k/yr from these totals. Unfortunately everything that follows from these calculations is flawed. Sorry gato.

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It's disgusting how our health 'leaders' are willing to go along with Pfizer & Company's definitions.

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Very good. I wrote a little thought experiment how the way we judge the vaccines would indeed show objectively crappy vaccines to be effective. The whole thing is a giant con.


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Several comments below claim that Gato made serious errors in this article. I think he owes us a response.

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You know who bought the story? Rafael Nadal.

You know who was wise enough not to? Novak Djokovic

You know who just got blown out of a match and complained of it feeling like "Needles" in his chest with every breath? Rafael Nadal, the same Rafael Nadal who said:

"He made his own decisions, and everybody is free to take their own decisions, but then there are some consequences,"

"From my point of view, that's the only thing that I can say is I believe in what the people who know about medicine say, and if the people say that we need to get vaccinated, we need to get the vaccine."

Yes indeed, Rafael. There are consequences.

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We know these are not really "vaccines." Instead of using the words "unvaccinated" or "boosted" or "fully vaccinated," from now on I will substitute variations on the word "poisoned."

I.e., "no, thankfully I have not been poisoned and I will do my utmost to avoid doing so."

"You are saying that you only feel safe when you are surrounded by other poisoned people such as yourself, and you are afraid of being around my high level of health?"

"You have received two doses of poison, in other words you are apparently fully poisoned."

"Perhaps you would like to be boosted with some more of the same poison you had previously, since you are not satisfied yet with the cumulative damage already done?"

"You are not 'up to date' on the level of poison in your system. Better keep it topped off and levels high for maximum damage."

Many of us are concerned with toxins in our environment, or that of our children. We try to eat organic, whole foods and avoid junk food. We use safe and natural cleaning products. This is like the junk food of medicine. It's fake, it is full of chemicals, you don't know exactly what it's doing to you, but it can't be anything good. All of your friends are taking it and you don't want to feel left out; you'd rather be a part of the cool kid group, even if they are a bunch of crackheads. You might be addicted to it, but you would be better off if you quit and find some better influences.

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the Mafia are not going to like this cat, i seem to recall in the Sopranos they killed Pussy, don't go boating!

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This has been the theme throughout these studies, most of which are published in the NEJM (which published surgisphere and whose chief editor is the one that wanted to experiment on children with the vaccine rollout). There is an inate survival bias in this per-protocol analysis instead of using intention-to-treat, which is what should have been done. The allocation of anyone who has received the treatment but when they die or are hospitalised to be counted in the non-treatment arm is scientific fraud. It's actually easy to tell that they have misrepresented the data because the hospitalisation and death rate for the "2-dose" group in the booster paper is the same as the "unvaccinated" in the original paper. So either the vaccine didn't work or they just use the miscategorisation trick against whichever group they are comparing their wonder drug to. Great stuff

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Good article, but some points of critisism, some of which you already mention:

1) Two years in, any data that doesn't take natural immunity into account is close to useless. You already state "thus got more advantage from greater pre-existing immunity". That is key. There are huge behavioural differences between vaxed, unvaxed and boosted, and as one aspect of that differences in if they 'had it'. natural immunity has been shown now definitive to be far supperiour to being vaxed and/or boosted.

2) Omiron became an upper respiratory illness, so humoral vaccines by definition do nothing. So any large difference in infections make me suspicious in general. It shows me that the data cohorts are not equal. An virus that can replicate in the throat outside reach of anything good or bad in the humoral parts of the body, shouldn't affect infection. Outcome yes, not infection. So either we have hit a revolutionary bad mechanism, or we are looking at exceptionally bad data. I know the first one is a popular topic on skeptic websites, but my money is on the first. Especially since the poor data, as explained in the first part of the article. Cohorts are not euql and not even stable unequal.

E.g. Two examples of where behaviour issues also play a role.

First, many of the boosted crowd have been the ones that also worked from home, hunkered down, avoided groups, etc. Now the restrictions are being lifted they are all getting infected in massive numbers. If you were unvaxed, your probably also the personality that didn't avoid large crowds, etc in the first place. That matters in terms of natural immunity.

Also for elderly who live in nursing homes they are all (forced/encouraged) vaxed, but if one of them or one of the caregiver gets omicron, the entire nursing home gets it. So hence you get huge waves. The living at home (more likely to be unvaxed or just double double vaxed) will benefit from these statistics.

And last, testing behaviour is non-symmetrical. Neurotic boosted are far more likely to get tested. So do people in nursing homes. Various studies showed a strong causality (which inherently is not just correlation) between testing quantity and testing positive quantity. So the testing rate must be equal to not get inherent higher infection quantity. In reverse if the testing rates are hence not equal between cohorts, you get different infection rates outcome.

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