[trimethylpurine]

You're not addressing the issue. 5% of what?

5% of symptomatic COVID cases. So now you have to compare that to only people with injuries from the vaccine.

Otherwise you're comparing apples to oranges.

If you want to compare people who got the vaccine to people who didn't, then you need to include ALL people who didn't. Not just the ones who were symptomatic.

And no, you can't include those people who had previous symptoms in the "everyone" group. Obviously those people are way more likely to return for treatment than the average Joe. They are skewing the sample because they are immediately nearly 100% likely to return for treatment. That is not representative of a random sample of people in the general population.

The question was about the general population, and they didn't sample the general population. They sampled people who come to the hospital. That is, only people with symptoms of some sort or another that are bad enough to warrant a journey to get treatment.

So all you can conclude is that people who come to the hospital have a higher frequency of heart conditions. And even that claim is not firmly established by the data, it's just a sensible inference. We expect that people who need treatment are more likely to seek treatment than those that don't need it. But there are countless counter examples there too. Hypochondria, etc.

No doubt if we had the data we'd find more problems there.

Wu's statement is fine, because again, it's not ALL people with COVID. His statement holds for people seeking treatment with COVID which is what they sampled.

Wu has no clue how many people have COVID asymptomatically and how to separate them from people without COVID altogether, and makes no claim about that.

You're simply making a claim that isn't in the research. Vaccination for people without COVID or with asymptomatic COVID is not researched or compared here.

This research, if you want to use it for a comparison, is for vaccination of people already coming to a hospital with symptoms of some kind.

EDIT: forgot to mention this was during a pandemic, so you might see significantly lower numbers for heart conditions if the study was repeated today. And it might be the case that during the pandemic it would have been a good idea to vaccinate everyone coming to a hospital from a statistical standpoint. But an even better take would be to identify what was common amongst the group with bad outcomes and vaccinate them, more specifically.

Regardless, nothing here implies better outcomes for everyone. It's not claimed by and it's not inferable from the research. And certainly not when there is a bunch of research showing contrary results to this study an example of which I linked earlier.

[croon]

No? It's population cohort study, not a hospital clinical sample. You're simply wrong in your assertions.

You can absolutely track asymptomatic infections through seroprevalence studies which we have been doing since 2020.

> forgot to mention this was during a pandemic, so you might see significantly lower numbers for heart conditions if the study was repeated today.

Yeah, that's making the point for me.

[trimethylpurine]

>Yeah, that's making the point for me.

Yeah, I'll help you make your point when it's accurate. That's called science.

Just fix the problems in your argument and I'll help you make it.

[trimethylpurine]

Respectfully, I think you've mistaken the definition of the word cohort . A cohort specifically does not include everyone. Please check the definition of cohort and then read section 2.4.

They tested people treated at a small collection of hospitals (the cohort).

And, you have made several other mistakes aside from that.

The 2020 studies are all retracted. They were over-spinning the centrifuges, I think by a factor of 10 or 100 maybe? They got insanely high positive numbers and the guidance was all updated to correct for that. You can ask an LLM to help you find the CDC publications on that. Don't use those numbers.

Additionally, even if your interpretation was accurate, it would fly in the face of thousands of other studies, just like the one I posted earlier in the thread.

Make sure your beliefs are based on peer reviewed research, that is not retracted, has been established long enough to withstand challenges from the scientific community, and that you're interpreting it honestly, using the established academic criteria for the scientific method and publication standards. Be careful to check the definition words that you're not familiar with, and even ones that you think you're familiar with. They might have a different meaning when used in a scientific context.

Out of curiosity, are you a journalist, college educated? I'm trying to understand where science has failed here. I would like our institutions to produce adults that can identify sound research and draw logical conclusions. There are some basic methods for doing that. I don't mean any offense by that. I'm just trying to understand what's broken. And more to the point, whether or not you are actually interested in science or you're just trying to prove that the media knows more than the scientists. Or maybe you just can't believe that journalists lie about what scientists say.

Is the media in the business of selling truth for profit, even if it means they won't profit? Do you believe that enough to take drugs that legally restrict you from taking legal action against the manufacturer when you're injured? (See CARES act)

Is your motivation to continue discussion political? Or scientific? If it's scientific, let's stick to the facts and follow the science. Don't try to make the science say what you want. You'll find that doesn't work in the broader forum.

[croon]

> Respectfully, I think you've mistaken the definition of the word cohort . A cohort specifically does not include everyone. Please check the definition of cohort and then read section 2.4.

> They tested people treated at a small collection of hospitals (the cohort).

In epidemiology, a cohort is simply a defined group of people followed over time. In a population-based cohort study (which this is), the cohort is the entire population of the health district (over 500,000 people).

The study did not just evaluate people treated at the hospital. It used the hospitals EHR to identify the numerator (the heart inflammation cases) out of the denominator (the entire regional population).

> The 2020 studies are all retracted. They were over-spinning the centrifuges, I think by a factor of 10 or 100 maybe? They got insanely high positive numbers and the guidance was all updated to correct for that. You can ask an LLM to help you find the CDC publications on that. Don't use those numbers.

You are confusing PCR testing with serology. The "over-spinning" you are referencing relates to PCR cycle thresholds (Ct values), which are used to detect active viral RNA swabs. Seroprevalence studies—which track historic asymptomatic spread—do not look for active RNA. They test blood serum for antibodies using immunoassays (like ELISA). They do not use PCR amplification, and they are absolutely not "all retracted." They are the standard of how we track the infection rate of a population.

> Is your motivation to continue discussion political? Or scientific? If it's scientific, let's stick to the facts and follow the science. Don't try to make the science say what you want.

This is... literally what I've been doing the entire time.

[trimethylpurine]

> a cohort is simply a defined group of people followed over time.

Yes, and the group is defined very clearly here, as is usually the case. I appreciate that you've conceded that it does not include everyone, as we see in your next quote:

> It used the hospitals EHR to identify the numerator (the heart inflammation cases) out of the denominator (the entire regional population).

So it compared something studied in the cohort (the numerator) to something not studied and outside the cohort (the denominator). So now let's establish whether or not the denominator can be used without calling ourselves science deniers.

And as we go, let's consider that a slight adjustment to that denominator has a multiplicative impact on the result! So we really want to get that dialed otherwise our interpretation could lie very, very far from the truth.

Your next quote leads us right to the most convincing point about the denominator.

> You are confusing PCR testing with serology.

I said all methods measuring prevalence in 2020 were wrong. That doesn't imply confusion with PCR. Serology also was wrong.

Just to find something agreeable to cite, here is the CDC stating very clearly that we shouldn't use serology for this kind of decision making. [0] "Negative results do not rule out SARS-CoV-2 infection and should not be used as the sole basis for treatment or patient management decisions, including infection control decisions."

So, assuming you trust the CDC publishes good science, then you know damned well you can't trust that denominator you keep throwing around as fact. At the very least, you're in opposition to CDC guidance if you want to keep using that denominator. You're not a science denier are you?

> This is... literally what I've been doing the entire time.

Well, you may want to read back. The claim that a cohort includes everyone is not remotely in the realm of science. Neither of us can conceive of an experiment that would obtain the denominator to be used in the math above. Not as it is, and definitely not if you defined it as "everyone else." And that's been my point for the last few comments. It's an illogical, fantasy based approach to interpreting the data. You did not test everyone, and you never will for any research ever in the past or future. It's not even fathomably possible.

The denominator, implicitly, makes broad assumptions about cultural, environmental, and economic factors, and that's just the beginning. There are countless other factors that we haven't even thought of yet. So that denominator, can be seen as a wild guess at best, and at worst it's a blatant falsehood if the study was published after whatever research the CDC is relying on to tell us not to use it. And therefore, it must be considered with all of the ambiguity that it implicates, as a wild guess or a blatant falsehood.

I don't like to take things that are wild guesses and stuff them into my math. There's a special word for that, pseudoscience. I have more respect for science than to do that.

So, all things considered, I'll take your word for it that sticking to the science is literally what you've been doing. And you can literally do it better by not using wild guesses to try to make the math say what you want.

If you will use numbers that are based on measured (or even measurable!) data, I'll be more inclined to lend belief to what insight you have that's worth exploring. And I hope you will.

--

Summary: If you have an argument that makes sense, I'll gladly follow it. But you're not there yet.

Your argument is entirely based on a denominator that doesn't remotely meet what modern philosophers of science would refer to as worth believing in. It's a wild guess that the CDC says is inaccurate for weighing this kind of decision.

[0] https://www.cdc.gov/covid/hcp/clinical-care/overview-testing...