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.
> 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.
> 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.
The denominator is literally the exact number of registered citizens in that district. There are no guesses, "wild" or otherwise.
Spain has a universal healthcare registry. Everyone living in that district has a medical record which can be continually tracked, which is used in this study.
The healthy people at home aren't "outside the cohort". You are arguing against a methodology you don't understand.
The CDC quote you linked tells doctors not to use antibody tests to triage acute patients because antibodies take weeks to form. It has nothing to do with retrospective seroprevalence studies, which the CDC (and WHO, and every major health organization in the world) itself uses to track population spread.
Do you have the prevalence numbers or not? You're not going to convince me that LATER we got better at testing and so you're using those test estimates to plug into this older study. That's not what was discussed in this research, obviously.
That wouldn't be scientific at all, would it?
I think you're trying to make the case that the estimates are good enough, but you haven't done a good job of substantiating that claim. You have provided no evidence other than your personal hearsay that the CDC and WHO agree on something that they never said. Go ahead and link to it if they did. I'll wait.
Separately you keep saying I misunderstand when it's clearly the opposite. Let me break it down for you clearly because, from my perspective, you are just plugging random bullshit into the wrong place in a math problem:
>The denominator is literally the exact number of registered citizens in that district. There are no guesses, "wild" or otherwise.
Right! But that's not what you're claiming. How does this tell us how many people were infected and asymptomatic? You are aware that "registered citizens in the district" is not the same is "infected people with no symptoms" right? So why are you plugging it into a math problem where "infected people with no symptoms" is the expected variable for substantiating your claim?
> Spain has a universal healthcare registry. Everyone living in that district has a medical record which can be continually tracked, which is used in this study.
Great! So tell us how many people were infected with no symptoms in 2020 so that we can make an effort to believe your claim that vaccines are less harmful than infection! It's pretty simple math, you just need the data that you claim to already have but refuse to share!
You are saying that COVID is worse than infection, so tell us when you tested people with no infection so that you can say that! When did you perform that test that you keep acting like is common sense knowledge? Why don't you just show us the number so we can stop the discussion and go home satisfied in our medical knowledge?
> The CDC quote you linked tells doctors not to use antibody tests to triage acute patients because antibodies take weeks to form.
It implies something about the data, even if you twist like you did. I personally prefer to go with the exact quote, which I gave you. But your twist on it isn't any better for your argument. It, in both cases, draws significant doubt about the accuracy of the test.
Let's pretend I have an allergy to the COVID protein. Should I use that test to make a decision about my vaccination?
I think you should go to prison if you said yes. Don't you? You'd be a murderer.
But you're going to use it to make a broader, less evidence based claim than that?
Summary:
Please provide the prevalence data. A bunch of people that you didn't test, whether you call them the cohort or not, does not establish prevalence. You need to include "asymptomatic infected" numbers to make your claim. As far as we know so far, you don't have those numbers. And, the CDC and the WHO don't even claim to have them, which is why your claim sounds so implausible. I mean, unless you're at the forefront of research at the CDC or something. But I don't see that this is the case.
> You are saying that COVID is worse than infection, so tell us when you tested people with no infection so that you can say that! When did you perform that test that you keep acting like is common sense knowledge? Why don't you just show us the number so we can stop the discussion and go home satisfied in our medical knowledge?
That's the neat part, you don't have to. Vaccinated people had a prevalence rate of 5/100k. Unvaccinated AND symptomatic covid had 200/100k. Unvaccinated and asymptomatic or uninfected at 70/100k.
It doesn't matter what fraction of the unvaccinated group was asymptomatic, because the total average of the group you are advocating to stay in (70) is still 14 times higher than the group you are telling people to avoid (5).
EDIT: "tell us when you tested people with asymptomatic infection"
Also, I'm mind bogglingly curious how you think those people would be discovered to perform a test on! So let me know why you even think that's possible! It seems like common sense that it wouldn't be because they don't exactly line up for testing and treatment when they feel perfectly fine.
My interpretation of the Spanish study allows for both articles to be totally valid.
Your interpretation requires that you think you are smarter than one of these groups of researchers. It requires that Stanford's study is wrong.
Honestly, which is more likely?
It's okay to learn something here. You thought the denominator was people that weren't infected. You didn't think that it might be a group of 100% infected. But it could be. We simply don't know.
It's okay to not have realized that. The scientific thing to do here is learn from your mistake.
More simply, in case you still aren't seeing it, Wu's claim that COVID is worse is based on an assumption about prevalence. He believes (or believed, like everyone did at the time) that COVID prevalence is a known variable. But it was later discovered that all of the prevalence data was damaged and by the time the tests were corrected, it had mutated. So we plainly have very little data about prevalence, and we can't go back in time to fix that.
Based on the data that was available to Wu at the time, he was correct. Based on what we know now, he is very likely not correct.
For all we know, literally every single person on Earth might have been infected. If you instead counted 8 billion infections, then the outcomes for covid infection look much better than the myocarditis risks associated with the vaccine.
We just don't know. You are making a faith based claim, without evidence. Simply put.
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.