That may or may not be the case, but you're rather detracting from the original comment, either deliberately or not.

The issue in the Alzheimer's world is the possibility that the very disease mechanism concept underlying the vast majority of research and interventional trials into which countless multiple billions have been poured, is incorrect.

Within that space, this is orders of magnitude more fundamental and serious than a flip aside that lots of trials have problems, so who cares about another?

> the very disease mechanism concept underlying the vast majority of research and interventional trials into which countless multiple billions have been poured, is incorrect.

Not "is incorrect," but might be incorrect. And we almost certainly won't know it is correct until we actually have a therapy.

Those pursuing cures could have waited until there was more solid science, but they and their funders took on the risk, knowing full well that the amyloid hypothesis is not proven.

This is not some indictment of science, this is normal risk taking for a problem that hugely affects society.

> Not "is incorrect," but might be incorrect.

That's why the part you quoted is preceded by "the possibility that".

But the entire framing of the comment is that the amyloid hypothesis is taken as fact and not possibility, when in fact the core research question is whether it is true or not.

It is the best possible explanation so far, but four decades of research have not reached a definitive conclusion.

An open problem is not a problem for science, that's the fundamental focus of science. The problem is people misrepresenting what science is and what it aims to do.

(Late back to this but) It's far more skewed than you're making out. That the a-beta hypothesis is true, is/was the vastly dominant prevailing belief in the field, to the extent that it hasn't been a question that many 'experts' were willing to meaningfully address.

To be clear: for decades, researchers wishing to pursue lines of inquiry contrary to the a-beta hypothesis struggled for traction and funding, and saw their careers struggle as a result[0]. As such, trying to disprove the a-beta hypothesis was not the core research question for many/most, for long time.

[0] https://www.statnews.com/2019/06/25/alzheimers-cabal-thwarte...

This article is just sensationalization of the standard scientific process. Grants do get awarded by friends and it does appear very much like a cabal. Or things like this:

> A top journal told one that it would not publish her paper because others hadn’t.

Oh the horror, not getting published in a top journal! Turns out that most good science gets published outside the top journals.

This sort of behavior is bad, and has always been part of the process, and may actually be better today than it was a century ago, as the clubs are not nearly so tight as they were back then.

Early in my career I remember reading some of ET Jaynes' (an early Bayesian reasoning guy) discussions of his early career, and how he had to very very carefully choose his topics so that he wouldn't upset the big personalities in physics and thereby have his entire career crushed. It's better these days than it was then!

There will be sour grapes about funding, just as there are when VCs all jump on the hype train for the same idea, but my only scientific exposure to the amyloid hypothesis for the past 20 years has been in terms of it being an unproven hypothesis. Starting down exploratory routes for explanatory hypotheses should have been pursued, and was pursued, and will continue to be pursued, but the question of "how much" is exceptionally difficult to answer.

Perhaps I'm biased from being in Science too long, but I've seen so many sensational Stat News article that never pan out when pushed upon. I wouldn't trust them at all with stuff like this.

Ok, this is awful, and clearly goes much deeper, and I'm starting to be very convinced of your position:

https://www.science.org/content/article/misconduct-concerns-...

Ah, the game of telephone.

>For more than 150 trials, Carlisle got access to anonymized individual participant data (IPD). By studying the IPD spreadsheets, he judged that 44% of these trials contained at least some flawed data: impossible statistics, incorrect calculations or duplicated numbers or figures, for instance. And in 26% of the papers had problems that were so widespread that the trial was impossible to trust, he judged — either because the authors were incompetent, or because they had faked the data.

Firstly, this is only from one journal, Anesthesiology. Second, the phrase "at least" indicates that while 44% had some amount of (presumably) flawed data, only 26% of the studies were bad enough to be judged fake or severely flawed by this one (admittedly esteemed) researcher in the field of anesthesiology. It's important to be skeptical and do your homework when you hear sweeping and/or shocking results. It's also important to read carefully, especially with science journalism because it is written for clicks and broad audiences, not to reduce ambiguity and adhere to strict standards of accuracy.

I didn’t go look up the quote but based on your version here it sounds like roughly half (44%) had some kind of suboptimality, and of those roughly a quarter (26%) had serious problems preventing them from being relied on.

That means 11% of the total papers should be discarded, which means 89% of the papers can be used.

As per usual, science reporting fails to use precise language. It can be interpreted either way, although I think your interpretation is the slightly larger leap based on phrasing. In any case, it is far below the 70% (and not directed broadly at all scientific research) that GP states.

I went and read the article but have not tracked down the original paper.

It is at least 26%, because that was the percentage of studies that provided access to their data that proved faked or fatally flawed.

It may be substantially higher.