JAMA

Article

JAMA is a recurring publication in the Astral Codex Ten archive, appearing 5 times across 5 issues between March 03, 2021 and August 14, 2025. The archive places it in contexts such as “was reviewed in JAMA”; “It was published in JAMA, one of the most prestigious US medical journals”; “Avi Bitterman et al formally published their … paper in JAMA”. It most often appears alongside Argentina, Twitter, Alexandros Marinos.

Metadata

Category: Publications
Mention count: 5
Issue count: 5
First seen: March 03, 2021
Last seen: August 14, 2025

Appears In

- Argentina (3 shared issues)
- Twitter (3 shared issues)
- Alexandros Marinos (2 shared issues)
- Avi Bitterman (2 shared issues)
- Colombia (2 shared issues)
- COVID (2 shared issues)
- Donald Trump (2 shared issues)
- Gideon Meyerowitz-Katz (2 shared issues)
- Italy (2 shared issues)
- Nature (2 shared issues)
- nostalgebraist (2 shared issues)
- Spain (2 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Links For March

March 03, 2021 · Original source

26: The Mongol In Our Midst was a 1920s pseudoscience book claiming that Down’s Syndrome (sometimes called mongolism because affected babies look kind of Asian if you’re racist and have a vivid imagination) was literally caused by relic Asian genes that Europeans got from Mongol hordes raping their ancestors. Seems to have been taken somewhat seriously at the time and was reviewed in JAMA and Nature, though I can’t access the reviews to see how critical they were.

Inline links: The Mongol In Our Midst

Ivermectin: Much More Than You Wanted To Know

November 17, 2021 · Original source

…looks very impressive, in terms of the experimental group doing better than the control, except that they don’t specify whether it was before the trial or after it, and at least one online commentator thinks it might have been before, in which case it’s only impressive how thoroughly they failed to randomize their groups. Overall I don’t feel bad throwing this study out. I hope it one day succeeds in returning to its home planet. Lopez-Medina et al: Colombian RCT. 200 patients took ivermectin, another 200 took placebo. They originally worried the placebo might taste different than real ivermectin, then solved this by replacing it with a different placebo, which is a pretty high level of conscientiousness. Primary outcome was originally percent of patients whose symptoms worsened by two points, as rated on a complicated symptom scale when a researcher asked them over the phone. Halfway through the study, they realized nobody was worsening that much, so they changed the primary outcome to time until symptoms got better, as measured by the scale. In the ivermectin group, symptoms improved that much after 10 days; in the placebo group, after 12, p = 0.53. By the end of the study, symptoms had improved in 82% of ivermectin users and 79% of controls, also insignificant. 4 patients in the ivermectin group needed to be hospitalized compared to 6 in the placebo group, again insignificant. This study is bigger than most of the other RCTs, and more polished in terms of how many spelling errors, photographs of computer screens, etc, it contains. It was published in JAMA, one of the most prestigious US medical journals, as opposed to the crappy nth-tier journals most of the others have been in. When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. Ivermectin proponents make some good arguments against it. In order to get as big as it did, Lopez-Medina had to compromise on rigor. Its outcome is how people self-score their symptoms on a hokey scale in a phone interview, instead of viral load or PCR results or anything like that. Still, this is basically what we want, right? In the end, we want people to feel better and less sick, not to get good scores on PCR tests. Also, it changed its primary outcome halfway through; isn’t that bad? I think maybe not; the reason we want a preregistered primary outcome is so that you don’t change halfway through to whatever outcome shows the results you want. The researchers in this study did a good job explaining why they changed their outcome, the change makes sense, and their original outcome would also have shown ivermectin not working (albeit less accurately and effectively). I don’t know of any evidence that they knew (or suspected) final results when switching to this new outcome, and it seems like the most reasonable new outcome to switch to. Finally, their original placebo tasted different from ivermectin (though they switched halfway through). This is one of the few studies where I actually care about placebo, because people are self-rating their symptoms. But realistically most of these people don’t know what ivermectin is supposed to taste like. Also, they did a re-analysis and found there was no difference between the people who got the old placebo and the new one. I’m making a big deal of this because ivmmeta.com - the really impressive meta-analysis site I’ve been going off of - puts a special warning letter underneath their discussion of this study, urging us not to trust it. They don’t do this for any of the other ones we’ve addressed so far - not the one by the guy whose other studies were all frauds, not the one where 50% of 21 people had headaches, not the unrandomized one where the groups were completely different before the experiment started, not even the one by the guy accused of crimes against humanity. Only this one. This makes me a lot less charitable to ivmmeta than I would otherwise be; I think it’s hard to choose this particular warning letter strategy out of well-intentioned commitment to truth. They just really don’t like this big study that shows ivermectin doesn’t work. Also, the warning itself irritates me, and includes paragraphs like: RCTs have a fundamental bias against finding an effect for interventions that are widely available — patients that believe they need treatment are more likely to decline participation and take the intervention [Yeh], i.e., RCTs are more likely to enroll low-risk participants that do not need treatment to recover (this does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable). This trial was run in a community where ivermectin was available OTC and very widely known and used. Nobody else worries about this, and there are a million biases that non-randomized studies have that would be super-relevant when discussing those, but somehow when they’re pro-ivermectin the site forgets to be this thorough. I think a better pro-ivermectin response to this study is to point out that all the trends support ivermectin. Symptoms took 10 days to resolve in the ivermectin group vs. 12 in placebo; 4 ivermectin patients were hospitalized vs. 6 placebo patients, etc. Just say that this was an unusually noisy trial because of the self-report methodology, and you’re confident that these small differences will add up to significance when you put them into a meta-analysis. Roy et al: We’re back in East India, and back to non-randomized trials. 56 patients were retrospectively examined; some had been given ivermectin + doxycycline, others hydroxychloroquine, other azithromycin, and others symptomatic treatment only. We don’t get any meaningful information about how this worked, but we are told that they did not differ in “clinical well-being reporting onset timing”. Whatever. Chahla et al: The first of many Argentine trials. 110 patients received medium-dose ivermectin; 144 were kept as a control (no placebo). This was “cluster randomized”, which means they randomize different health centers to either give the experimental drug or not. This is worse than regular randomization, because there could be differences between these health centers (eg one might have better doctors who otherwise give better treatment, one might be in the poor part of town and have sicker patients, etc). They checked to see if there were any differences between the groups, and it sure looks like there were (the experimental group had twice as many obese people as the controls), but as per them, these differences were not statistically significant. Note that if this did make a difference, it would presumably make ivermectin look worse, not better. The primary outcome was given as “increase discharge from outpatient care with COVID-19 mild disease”. This favored the treatment; only 2/110 patients in the ivermectin group failed to be discharged, compared to 20 patients in the control group. But, uh, these were at different medical centers. Can’t different medical centers just have different discharge policies? One discharges you as soon as you seem to be getting better, the other waits to really make sure? This is an utterly crap endpoint to do a cluster randomized controlled trial on. If you’re going to do cRCT, which is never a great idea, you should be using some extremely objective endpoint that doctors and clinic administrators can’t possibly affect, like viral load according to some third-party laboratory, using the same third-party laboratory for both clinics. This is such a bad idea that I can’t help worrying I’m missing or misunderstanding something. If not, this is dumb and bad and should be ignored. Mourya et al: We’re back in India. This is a nonrandomized study comparing 50 patients given ivermectin to 50 patients given hydroxychloroquine. No primary outcome was named, but they focus on PCR negativity. Only 6% of patients in the hydroxychloroquine group were negative, compared to 90% of patients in the ivermectin group! On what day did they do the test? Uh, kind of random, and they admit that “in [the hydroxychloroquine group], mean time difference from the date of initiation of treatment and second test was significantly longer (7.24±2.75 days) as compared to 5.22±1.21 days in [the ivermectin group] (p=0.021).” Since they assessed these groups at different times, we shouldn’t draw any conclusions from them getting different results. Except that as far as I can tell this should handicap ivermectin, making it especially impressive that it did better. But also, the ivermectin group was made mostly of people who had been asymptomatic at the beginning (70%), and the hydroxychloroquine group had almost no asymptomatic cases (8%) . They were giving the ivermectin to healthy people and the hydroxychloroquine to sick people! They admit deep in the discussion that this “may be a confounding factor”. So basically they got totally different groups of people, tested them at totally different times, and the two sets of test results differed. So what? So this is why normal people do RCTs instead of whatever the heck this is, that’s what. Loue et al: …this one isn’t going to be an RCT either. Loue tells a story about a cluster of COVID cases at the French nursing home where he works. He asked people if they wanted to try ivermectin; 10 did and 15 didn’t. 1 ivermectin patient died, compared to 5 non-ivermectin patients. The non-ivermectin group looked a bit sicker than the ivermectin group in the inevitable Table 1, though it’s hard to tell. One interesting possible confounder (not mentioned, but I’m imagining it) is that demented patients probably couldn’t consent to ivermectin and ended up in the control group. This is another case of “I’m not going to trust anything that isn’t an RCT”. Merino et al: Another (sigh) non-RCT. Mexico City tried a public health program where if you called a hotline and said you had COVID, they sent you an emergency kit with various useful supplies. One of those supplies was ivermectin tablets. 18,074 people got the kit (and presumably some appreciable fraction took the ivermectin, though there’s no way to prove that). Their control group is people from before they started giving out the kits, people from after they stopped giving out the kits, and people who didn’t want the kits. There are differences in who got COVID early in the epidemic vs. later, and in people who did opt for medical kits vs. didn’t. To correct these, the researchers tried to adjust for confounders, something which - as I keep trying to hammer home again and again - never works. They found that using the kit led to a 75% or so reduction in hospitalization, though they were unable to separate out the ivermectin from the other things in the kit (paracetamol and aspirin), or from the placebo effect of having a kit and feeling like you had already gotten some treatment (if I understand right, the decision to go to the hospital was left entirely to the patient). I think this study is a moderate point in favor of giving people kits in order to prevent hospital overcrowding, but I’m not willing to accept that it tells us much about ivermectin in particular. Faisal et al: This one was published in The Professional Medical Journal (mispelled as “Profesional Medical Journal” in its URL), so you know it’s going to be good! It describes itself as “a cross-sectional study”, but later says it “randomized patients into two groups”, which would make it an RCT - I think they might just be using the term “cross-sectional” different from the standard American usage. A hospital in Pakistan got 50 patients on ivermectin + azithromycin, and another 50 on azithromycin alone. Primary outcome was not mentioned, and the data were presented confusingly, but a typical result is that only 4% of the ivermectin group had symptoms lasting more than 10 days, whereas 16% of the control group did, p < 0.01. They do a really weird thing where they compare how long it took symptoms to resolve between IVM and control groups within each bin. That is, if I’m understanding correctly, they ask “of the people who took between 3-5 days for symptoms to resolve, did they resolve faster for IVM or control?”. This is an utterly bizarre analysis to perform, although it doesn’t affect the fact that their other results still seem to favor ivermectin. Maybe I’m confused about what’s going on here. I’ve mostly been letting people off easy on no placebo, but I as far as I can tell (not very far) this paper seems to be going off whether patients reported continuing to have symptoms to the hospital doing the study, and I think that is potentially susceptible to placebo effects. Additionally, there’s no preregistration, and even though they talk a lot about doing PCR tests they don’t present the results. This is by no means the worst study here but I still think it’s pretty low quality and I don’t trust it. Aref et al: This one is published in the International Journal Of Nanomedicine, even though I’m pretty sure that isn’t a real thing. In this case the “nanomedicine” is a new nasal spray version of ivermectin which is so confusing I cannot for the life of me figure out what dose they are giving these patients. This Egyptian study gives 57 patients intranasal ivermectin plus hydroxychloroquine, azithromycin, oseltamavir, and some vitamins; another 57 patients get all that stuff except the ivermectin. Primary outcome is not stated, but they look at various symptoms, all of which look better in the ivermectin group: 95% of ivermectin patients got negative PCRs at some time point, compared to 75% of controls, p = 0.004. I am pretty suspicious of this study, not least because it comes from Egypt which has an awful reputation for fake studies, and it returns extreme results that I wouldn’t expect even if ivermectin was actually a wonder drug. But I cannot find any particular thing wrong with it, nor did anyone else I looked at, so I will grudgingly let it stand. Krolewiecki et al: Another Argentine study. This one is a real RCT. 30 patients received ivermectin, 15 were the control group (no placebo, again). Primary outcome was difference in viral load on day 5. The trend favored ivermectin but it was not statistically significant, although they were able to make it statistically significant if they looked at a subset of higher-IVM-plasma-concentration patients. They did not find any difference in clinical outcomes. A pro-ivermectin person could point out that in the subgroup with the highest ivermectin concentrations, the drug seemed to work. A skeptic could point out that this is exactly the kind of subgroup slicing that you are not supposed to do without pre-registering it, which I don’t think this team did. I agree with the skeptic. Vallejos et al: Another Argentine study. It’s big (250 people in each arm). It’s an RCT. It tries to define a primary outcome (“Primary outcome: the trial ended when the last patient who was included achieved the end of study visit”), but that’s not what “primary outcome” means, and they don’t offer an alternative. Other outcomes: no difference in PCR on days 3 or 12. Hospitalization is nonsignificantly better in the ivermectin group (14 vs. 21, p = 0.2), but death is nonsigificantly better in the placebo group (3 vs. 4, p = 0.7). This isn’t even the kind of nonsignificant that might contribute to an exciting meta-analysis later. This is just a pure null result. I cannot find any problem with this study, and neither can anyone else I checked. This is the biggest RCT we’ve seen so far, so we should take it seriously. TOGETHER Trial: Speaking of big RCTs… This one hasn’t been published yet. There’s a video of a talk about it, but I am not going to watch it, because it is a video, so I am getting information secondhand from eg here. Apparently, it compares 677 people (!) randomized to ivermectin to 678 people randomized to placebo. 86 ivermectin patients ended up in the hospital compared to 95 placebo patients, p-value not significant. This was a really big professional trial done by bigshot researchers from a major Canadian university, and the medical establishment is taking it much more seriously than any of these others. When it comes out, it will probably get published in a top journal. When discussing Lopez-Medina, I wrote: When people say things like “sure, a lot of small studies show good results for ivermectin, but the bigger and more professional trials don’t”, this is one of the two big professional trials they’re talking about. This is the other one. Not coincidentally, it’s also the other trial that ivmmeta.com has a warning letter underneath telling you to disregard. Their main concern is that instead of truly randomizing patients to ivermectin vs. placebo, they did a time-dependent randomization that meant during some weeks more patients were getting one or the other. This is a problem because the trial takes place in Brazil, where different variants were more common at different times. Here’s their image: On the one hand, I have immense contempt for ivmmeta for letting all those other awful studies pass and then pulling out all the stops to try to nitpick this one. I have no idea if their proposed randomization failure really happened. And no doubt the reason they’re even able to investigate this is that this study is really careful and transparent - most of them don’t tell you anything about their randomization method. I would be shocked if other studies don’t have all these problems and worse. On the other hand, the point isn’t to be fair, it’s to be right. And this is a potential confounder. Not a huge one. But a potential one. I guess all we can do is try to bound the damage. Even if the confounding is 100% real and bad, there’s no way to make this study consistent with the crazy super-pro-ivermectin results of studies like Espitia-Hernandez and Aref. And even if we deny any confounding, we see the same slight pro-ivermectin trend - 86 hospitalizations vs. 95 - that we’ve seen in so many other studies. Nothing is going to make me believe that this isn’t in the top 33% of studies we’ve been looking at, so let’s add it as grist for the meta-analysis (though maybe not quite as much grist as its vast size indicates) and move on, angrily. Buonfrate et al: An Italian RCT. Patients were randomized into low-dose ivermectin (32), placebo (29), or high-dose ivermectin (32). Primary outcome was viral load on day 7. There was no significant difference (average of 2 in ivermectin groups, 2.2 in placebo group). They admit that they failed to reach the planned sample size, but did a calculation to show that even if they had, the trial could not have returned a positive result. Clinically, an average of 2 patients were hospitalized in each of the ivermectin arms, compared to 0 in the placebo arm - which bucks our previously-very-constant pro-ivermectin trend. Mayer et al: Not an RCT. Patients in an Argentine province were offered the opportunity to try ivermectin; 3266 said yes and become the experimental group, 17966 said no and became the control group. There were many obvious differences between the groups, but they all seemed to handicap ivermectin. There was a nonsignificant trend toward less hospitalization and significantly less mortality (1.5% vs. 2.1%, p = 0.03). While looking into this study, I learned the term “immortal time bias”. This means a period in between selection for the study and the beginning of study recording where patient outcomes are not counted. I think the problem here is that if you signed up for the system on Day X, and if you got sick before they could give you ivermectin, you were in the control group. See this Twitter thread, I have not confirmed everything he says. This only hardens my resolve to stay away from non-RCTs. Borody et al: Our last paper! …is it a paper? I can’t find it published anywhere. It mostly seems to be on news sites. Doesn’t look peer-reviewed. And it starts with “Note that views expressed in this opinion article are the writer’s personal views”. Whatever. 600 Australians were treated with ivermectin, doxycycline, and zinc. The article compares this to an “equivalent control group” made of “contemporary infected subjects in Australia obtained from published Covid Tracking Data”; this is not how you control group, @#!% you. Then it gets excited about the fact that most patients had better symptoms at the end of the ten-day study period than the beginning (untreated COVID resolves in about ten days). Why are these people wasting my time with this? Let’s move on. The Analysis If we remove all fraudulent and methodologically unsound studies from the table above, we end up with this: Gideon Meyerowitz-Katz, who investigated many of the studies above for fraud, tried a similar exercise. I learned about his halfway through, couldn’t help seeing it briefly, but tried to avoid remembering it or using it when generating mine (also, I did take the result of his fraud investigations into account), so they should be considered not quite independent efforts. His looks like this: He nixed Chowdhury, Babaloba, Ghauri, Faisal, and Aref, but kept Szenta Fonseca, Biber (?), and Mayer. There was correlation of 0.45, which I guess is okay. I asked him about his decision-making, and he listed a combination of serious statistical errors and small red flags adding up. I was pretty uncomfortable with most of these studies myself, so I will err on the side of severity, and remove all studies that either I or Meyerowitz-Katz disliked. We end up with the following short list: We’ve gone from 29 studies to 11, getting rid of 18 along the way. For the record, we eliminated 2/19 for fraud, 1/19 for severe preregistration violations, 10 for methodological problems, and 6 because Meyerowitz-Katz was suspicious of them. …but honestly this table still looks pretty good for ivermectin, doesn’t it? Still lots of big green boxes. Meyerowitz-Katz accuses ivmmeta of cherry-picking what statistic to use for their forest plot. That is, if a study measures ten outcomes, they sometimes take the most pro-ivermectin outcome. Ivmmeta.com counters that they used a consistent and reasonable (if complicated) process for choosing their outcome of focus, that being: If studies report multiple kinds of effects then the most serious outcome is used in calculations for that study. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. I’m having trouble judging this, partly because Meyerowitz-Katz says ivmmeta has corrected some earlier mistakes, and partly because there really is some reasonable debate over how to judge studies with lots of complicated endpoints. By this point I had completely forgotten what ivmmeta did, so I independently coded all 11 remaining studies following something in between my best understanding of their procedure and what I considered common sense. The only exception was that when the most severe outcome was measured in something other than patients (ie average number of virus copies per patient), I defaulted to one that was measured in patients instead, to keep everything with the same denominator. My results mostly matched ivmmeta’s, with one or two exceptions that I think are within the scope of argument or related to my minor deviations from their protocol. Placebo vs. ivermectin groups sometimes differed in size, which I’ve adjusted for and rounded off. Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant. This is maybe unfair, because there aren’t a lot of deaths in the sample, so by focusing on death rather than more common outcomes we’re pointlessly throwing away sample size. What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? I’ve chosen “most reasonable” as a balance between “is the most severe” and “has a lot of data points”: Now it’s p = 0.04, seemingly significant, but I had to make some unprincipled decisions to get there. I don’t think I specifically replaced negative findings with positive ones, but I can’t prove that even to myself, let alone to you. [UPDATE 5/31/22: A reader writes in to tell me that the t-test I used above is overly simplistic. A Dersimonian-Laird test is more appropriate for meta-analysis, and would have given 0.03 and 0.005 on the first and second analysis, where I got 0.15 and 0.04. This significantly strengthens the apparent benefit of ivermectin from ‘debatable’ to ‘clear’. I discuss some reasons below why I am not convinced by this apparent benefit.] (how come I’m finding a bunch of things on the edge of significance, but the original ivmmeta site found a lot of extremely significant things? Because they combined ratios, such that “one death in placebo, zero in ivermectin” looked like a nigh-infinite benefit for ivermectin, whereas I’m combining raw numbers. Possibly my way is statistically illegitimate for some reason, but I’m just trying to get a rough estimate of how convinced to be) So we are stuck somewhere between “nonsignificant trend in favor” and “maybe-significant trend in favor, after throwing out some best practices”. This is normally where I would compare my results to those of other meta-analyses made by real professionals. But when I look at them, they all include studies later found to be fake, like Elgazzar, and unsurprisingly come up with wildly positive conclusions. There are about six in this category. One of them later revised their results to exclude Elgazzar and still found strong efficacy for ivermectin, but they still included Niaee and some other dubious studies. The only meta-analysis that doesn’t make these mistakes is Popp (a Cochrane review), which is from before Elgazzar was found to be fraudulent, but coincidentally excludes it for other reasons. It also excludes a lot of good studies like Mahmud and Ravakirti because they give patients other things like HCQ and azithromycin - I chose to include them, because I don’t think they either work or have especially bad side effects, so they’re basically placebo - but Cochrane is always harsh like this. They end up with a point estimate where ivermectin cuts mortality by 40% - but say the confidence intervals are too wide to draw any conclusion. I think this basically agrees with my analyses above - the trends really are in ivermectin’s favor, but once you eliminate all the questionable studies there are too few studies left to have enough statistical power to reach significance. Except that everyone is still focusing on deaths and hospitalizations just because they’re flashy. Mahmud et al, which everyone agrees is a great study, found that ivermectin decreased days until clinical recovery, p = 0.003? So what do you do? This is one of the toughest questions in medicine. It comes up again and again. You have some drug. You read some studies. Again and again, more people are surviving (or avoiding complications) when they get the drug. It’s a pattern strong enough to common-sensically notice. But there isn’t an undeniable, unbreachable fortress of evidence. The drug is really safe and doesn’t have a lot of side effects. So do you give it to your patients? Do you take it yourself? Here this question is especially tough, because, uh, if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout “horse dewormer!” at you and compare you to Josef Mengele. But good doctors aren’t supposed to care about such things. Your only goal is to save your patient. Nothing else matters. I am telling you that Mahmud et al is a good study and it got p = 0.003 in favor of ivermectin. You can take the blue pill, and stay a decent respectable member of society. Or you can take the horse dewormer pill, and see where you end up. In a second, I’ll tell you my answer. But you won’t always have me to answer questions like this, and it might be morally edifying to observe your thought process in situations like this. So take a second, and meet me on the other side of the next section heading. … … … … … The Synthesis Hopefully you learned something interesting about yourself there. But my answer is: worms! As several doctors and researchers have pointed out (h/t especially Avi Bitterman and David Boulware), the most impressive studies come from places that are teeming with worms. Mahmud from Bangladesh, Ravakirti from East India, Lopez-Medina from Colombia, etc. Here’s the prevalence of roundworm infections by country (source). But alongside roundworms, there are threadworms, hookworms, blood flukes, liver flukes, nematodes, trematodes, all sorts of worms. Add them all up and somewhere between half and a quarter of people in the developing world have at least one parasitic worm in their body. Being full of worms may impact your ability to fight coronavirus. Gluchowska et al write: Helminth [ie worm] infections are among the most common infectious diseases. Bradbury et al. highlight the possible negative interactions between helminth infection and COVID-19 severity in helminth-endemic regions and note that alterations in the gut microbiome associated with helminth infection appear to have systemic immunomodulatory effects. It has also been proposed that helminth co-infection may increase the morbidity and mortality of COVID-19, because the immune system cannot efficiently respond to the virus; in addition, vaccines will be less effective for these patients, but treatment and prevention of helminth infections might reduce the negative effect of COVID-19. During millennia of parasite-host coevolution helminths evolved mechanisms suppressing the host immune responses, which may mitigate vaccine efficacy and increase severity of other infectious diseases. Treatment of worm infections might reduce the negative effect of COVID-19! And ivermectin is a deworming drug! You can see where this is going… The most relevant species of worm here is the roundworm Strongyloides stercoralis. Among the commonest treatments for COVID-19 is corticosteroids, a type of immunosuppresant drug. The types of immune responses it suppresses do more harm than good in coronavirus, so turning them off limits collateral damage and makes patients better on net. But these are also the types of immune responses that control Strongyloides. If you turn them off even very briefly, the worms multiply out of control, you get what’s called “Strongyloides hyperinfection”, and pretty often you die. According to the WHO: The current COVID-19 pandemic serves to highlight the risk of using systemic corticosteroids and, to a lesser extent, other immunosuppressive therapy, in populations with significant risk of underlying strongyloidiasis. Cases of strongyloidiasis hyperinfection in the setting of corticosteroid use as COVID-19 therapy have been described and draw attention to the necessity of addressing the risk of iatrogenic strongyloidiasis hyperinfection syndrome in infected individuals prior to corticosteroid administration. Although this has gained importance in the midst of a pandemic where corticosteroids are one of few therapies shown to improve mortality, its relevance is much broader given that corticosteroids and other immunosuppressive therapies have become increasingly common in treatment of chronic diseases (e.g. asthma or certain rheumatologic conditions). So you need to “address the risk” of strongyloides infection during COVID treatment in roundworm-endemic areas. And how might you address this, WHO? Treatment of chronic strongyloidiasis with ivermectin 200 µg/kg per day orally x 1-2 days is considered safe with potential contraindications including possible Loa loa infection (endemic in West and Central Africa), pregnancy, and weight <15kg. Given ivermectin’s safety profile, the United States has utilized presumptive treatment with ivermectin for strongyloidiasis in refugees resettling from endemic areas, and both Canada and the European Centre for Disease Prevention and Control have issued guidance on presumptive treatment to avoid hyperinfection in at risk populations. Screening and treatment, or where not available, addition of ivermectin to mass drug administration programs should be studied and considered. This is serious and common enough that, if you’re not going to screen for it, it might be worth “add[ing] ivermectin to mass drug administration programs” in affected areas! Dr. Avi Bitterman carries the hypothesis to the finish line: First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway About ten years ago, when the replication crisis started, we learned a certain set of tools for examining studies. Check for selection bias. Distrust “adjusting for confounders”. Check for p-hacking and forking paths. Make teams preregister their analyses. Do forest plots to find publication bias. Stop accepting p-values of 0.049. Wait for replications. Trust reviews and meta-analyses, instead of individual small studies. These were good tools. Having them was infinitely better than not having them. But even in 2014, I was writing about how many bad studies seemed to slip through the cracks even when we pushed this toolbox to its limits. We needed new tools. I think the methods that Meyerowitz-Katz, Sheldrake, Heathers, Brown, Lawrence and others brought to the limelight this year are some of the new tools we were waiting for. Part of this new toolset is to check for fraud. About 10 - 15% of the seemingly-good studies on ivermectin ended up extremely suspicious for fraud. Elgazzar, Carvallo, Niaee, Cadegiani, Samaha. There are ways to check for this even when you don’t have the raw data. Like: The Carlisle-Stouffer-Fisher method: Check some large group of comparisons, usually the Table 1 of an RCT where they compare the demographic characteristics of the control and experimental groups, for reasonable p-values. Real data will have p-values all over the map; one in every ten comparisons will have a p-value of 0.1 or less. Fakers seem bad at this and usually give everything a nice safe p-value like 0.8 or 0.9.

Links For April

April 14, 2022 · Original source

15: Ivermectin updates: the big Brazilian study that showed ivermectin doesn’t work was officially released. This doesn’t update my analysis because I had included a preliminary version of it. See Gideon Meyerowitz-Katz’s take on some objections here. Another big study from Malaysia also came out; the headline result is “doesn’t work” but Meyerowitz-Katz thinks it’s more complicated (although still leans negative). Avi Bitterman et al formally published their “ivermectin efficacy only in areas with parasitic worms” paper in JAMA. Alexandros Marinos still thinks it works.

Inline links: was officially released, here, also came out, thinks it’s more complicated, paper in JAMA, still thinks it works

Links For November 2024

November 01, 2024 · Original source

30: Did you know: if you Google “cool websites”, our subreddit (r/slatestarcodex) is the first result. 31: Moshe Koppel, who works at the intersection of computer science and Talmud, is writing a series of posts (presumably) based off of my Every Bay Area House Party, titled Jerusalem Area House Party (it’s multiple part, you have to go to the main Substack page to find the others). I won’t necessarily link everyone who riffs off one of my posts - but honestly I probably will if you also have a Wikipedia page that describes you as working on computational Talmudology. 32: David Roman says it’s a myth that Arabic scholars rescued and preserved the works of the great classical authors. 33: Medications often decrease “secondary endpoints” (eg stroke, heart attack), but the holy grail of pharma studies is proving that a certain drug decreases all-cause mortality. This is much harder (not all heart attacks kill people, and people die from lots of other things), but is the strongest possible endorsement for the drug (without it, you might worry that it only prevented non-fatal heart attacks, or that it killed as many people through side effects as it saves through heart attack prevention). Even great medications that we’re confident in can’t always clear this bar. But a new JAMA article adds another member to this select club: Adderall decreases all-cause mortality in ADHD, probably because it prevents drug addiction, car accidents, and impulsive actions. 34: Before the Gulf War got in the way, Saddam Hussein was building some crazy mosques: 35: Italy bans surrogacy - quite strictly, too, Italians aren’t even allowed to go abroad and do it. I am so sorry for all the Italians who will never get to be mothers and fathers because their government hates progress. You might hope that, whatever the other disadvantages of anti-immigrant parties, at least they’re incentivized to let natives have children, but looks like they can’t even get that one right. Starting to wonder whether the trains even run on time. 36: Elsewhere in “Italy sucks” news - did you know Italy’s tax code effectively bans startups? Companies are taxed before making any money, based on how many assets they have. If they have lots of assets but aren’t making money (eg because they’re still doing research / in stealth) then tax officials get confused and hostile and run increasingly punitive audits. Related: size of the European tech sector. It’s the red line on this chart; if you can’t see a red line at your screen resolution, then you’ve learned something important about the the EU tech sector. 37: Seen on @cremieuxrecuel’s twitter (preliminary, needs replication): Jews may have gone from 65-29 Democrat/Republican in 2020 to 58-40 this election. 38: Extelligence has a post responding to my critique of the cultural Christianity argument (among, uh, many other things), but I don’t really think it connects. I’m not telling atheists they can’t go to church/synagogue if it makes them feel happy and fulfilled - I’ve done this myself sometimes. My post was meant to argue against the claim that, for pragmatic reasons, atheists should support the Christianization of society as a defense against Islam or postmodernism or some other philosophical enemy. 39: Related: Extelligence is finally going for their Trust Assembly project/idea/startup for online consensus-based truth-seeking (I think something like a cross between Community Notes and Wikipedia, but as a browser extension, and for everything). He’s looking for potential developers/testers/users. 40: Jiankui He is the Chinese geneticist who made history with the first germline gene editing in humans (resulting in three babies supposedly immune to AIDS, although nobody has tested this). China sentenced him to three years in prison for unauthorized experimentation, but now he’s out of jail, has an English-language Twitter account, has a new lab, wants to work on Alzheimers, and seems pretty based (although not infinitely based): 41: Anthropic has a new version of their AI Claude which can use your computer. You give it permission, put it on a virtual desktop, and ask it to do things for you (eg “please find and download a picture of a cat” or “please research these ten things and put them in a text file”.) It moves your cursor, browses the Internet, and creates and saves files. People keep saying they’ll care about AI “when it operates autonomously” or “when it becomes an agent”. But this is a trivial barrier, and one which Computer Use Claude has arguably already passed. So far this feature is limited to developers (though anyone with computer knowledge can sign up for it) but I expect it to be the near future of consumer AI, to get better quickly, and to shade gradually into the “autonomous” “agentic” AI that you all think will require a paradigm shift. 42: Claim (from the IDF): Hamas faked polls showing that most Palestinians supported the October 7 attack; the real numbers are 31% in favor, 64% against. 43: Otto von Bismarck wanted to trick France into declaring war on Germany. In order to provoke the French, he sent the Ems Dispatch, a statement describing recent diplomatic events in a way that sounded maximally offensive. The French were so offended that “crowds” in Paris demanded war, and the Franco-Prussian War was declared soon afterwards. The part of this that I find most interesting is the text of the dispatch itself, which read: After the news of the renunciation of the Prince von Hohenzollern had been communicated to the Imperial French government by the Royal Spanish government, the French Ambassador in Ems made a further demand on His Majesty the King that he should authorize him to telegraph to Paris that His Majesty the King undertook for all time never again to give his assent should the Hohenzollerns once more take up their candidature. His Majesty the King thereupon refused to receive the Ambassador again and had the latter informed by the Adjutant of the day that His Majesty had no further communication to make to the Ambassador. I’m fascinated by the idea that only 150 years ago, it was obvious that if someone sent you this statement, you had to declare war or abandon all honor. If I read it carefully, I can sort of parse out that it sounds like the Prussians are unhappy, but that’s the most emotion I gather from it. Anyway, the Franco-Prussian War led to World War I which led to World War II - so if you don’t like 50 million people dying and the total devastation of Europe, blame this statement about ambassadors. 44: The first use of artificial insemination in humans: The first recorded case of artificial insemination by donor didn’t occur until 1884, when Dr. William Pancoast decided to treat a couple’s infertility by secretly inseminating the woman with sperm obtained from a medical student. The insemination happened while the patient was under anesthesia and Dr. Pancoast did not tell her what had occurred. She gave birth to a baby boy nine months later, but it was several years before the doctor finally confessed to her husband what he had done. Neither man ever informed the mother. It was 25 years later the result of this case was published. Dr. Pancoast was roundly condemned for his actions, but it did open the door for consensual sperm donor insemination. 45: ClearerThinking administers several personality tests to the same people to learn more about their comparative accuracy. I am most interested in their finding that tests with “factors” (eg the Big Five, where you rate people on a numeric scale) are inherently more accurate than those with “types” (eg Myers-Briggs, where you assign someone a specific category) and that, adjusting for this, Big Five is no more predictive than the Enneagram: 46: In 2022, I wrote Whither Tartaria, where I asked why ornate classical styles switched to more austere modernist styles around 1900 - 1950 in a variety of different arts (painting, architecture, literature, poetry, etc). I proposed seven theories, but was unsure which if any were true. Since then, Samuel Hughes of Works In Progress has been investigating. In May, he wrote a well-researched article showing that it wasn’t just increasing cost, because ornate classical architecture now costs less than ever. Now in a new article he demolishes a different theory - it’s not just decreasing cost (and subsequent lack of ability to signal wealth) - because costs didn’t decrease in several other arts, and the change was led by artists with rich people as reluctant followers. He concludes: Modernism may well be a status game of some kind; it may well signal taste more than it signals wealth; and this latter feature may be one of the things that distinguishes it from older artistic styles. But the mechanism by which this change came about must be different to the one Alexander describes. 47: Sort of kind of related - When Hamilton Lost Its Snob Appeal. The musical Hamilton was briefly an artistic/cultural phenomenon, but tastemakers eventually switched to making fun of it. Why? Rob Henderson says it happened after ticket prices came down and the common people could enjoy it. I disagree: everyone I knew who was into Hamilton got into it from the free online soundtrack long before they’d seen the show; I think this is more likely the usual fad cycle where anybody who’s too into yesterday’s fad is behind the curve and therefore uncool. 48: Related: Why are people such jerks to public intellectuals? And more. I agree this is a great mystery. 49: Some prominent Substack psychiatrists doing a video Q&A, submit your questions here. 50: Naomi Kanakia: The Literacy Delusion had a number of explanations for why reading books seemed to be so much worse for human beings (in terms of emotional wellness and productivity) than other forms of narrative entertainment, but its main theory was the integration hypothesis. That the stream of words in a book trained the human brain into a habit of self-consciousness, that reading books forced human beings to think of themselves as a stream of text, processed through time, making a coherent argument of some sort. And that this overall flattening effect forced readers to ignore aspects of their personality or their situation that were not otherwise in line with the overarching story they'd created about themselves. Basically, reading books causes repression and neurosis. The Literacy Delusion argued that, yes, human beings are storytelling machines, but that a stream of written text is a particular kind of story—a story that is particularly flat, particularly devoid of conflicting or harmonizing information—and that this flatness creates a peculiar effect on the human brain. 51: Last month, I linked Sasha Gusev’s No, Intelligence Is Not Like Height and asked people who disagreed to share their arguments; they sure did. First, several people pointed me to a new preprint, Family-GWAS Reveals Effects Of Environment And Mating On Genetic Associations, which finds that one of the main papers Gusev cited to make his case, Howe 2022, made a mistake - imputing sibling genotypes using a process designed for non-sibling genotypes - and that once that mistake is corrected, the finding disappears and intelligence and height appear similar. Second, Joseph Bronski has a more specific post where he responds to Gusev’s points one by one. He accuses Gusev of “[making] up his own chart to remove the error bars [from the originals], to obscure the fact that the study found no evidence for this in IQ”, and says that the cases where he didn’t do that are just “population stratification and range restriction”. Third, Noah Carl at Aporia, instead of writing a direct response like Bronski, argues that the usual method of attacking twin studies is obsolete; not only have the most-debated assumptions behind twin studies been thoroughly validated, but there are now other lines of evidence besides twin studies which confirm high IQ heritability. Fourth, Leonardo Parro (not framed as a response to Gusev) goes into more depth about one of those ways, a “pedigree-based analysis” demonstrating heritability of 54 - 69%, ie no “missing heritability” compared to twin studies. He summarizes this as the effect of “rare variants” compared to the usual SNPs - ie if you only look at the most common genes that are easiest to find, you get “missing heritability” compared to twin studies, but if you widen your search to rare genes that are hard to find, you don’t. 52: Extremely related: Heliospect is a startup promising polygenic selection for IQ and other traits; they were trying to stay in stealth mode but The Guardian spied on them and nonconsensually revealed their existence. The discussion on the r/ssc subreddit centered on their claim that (given enough embryos to choose from) they could increase a baby’s expected IQ by 6 points (I’ve also heard 7.5). Sasha Gusev had previously argued that current technology maxed out at 3.5 and future technology would max out at 6, so a claim of 6 - 7.5 is pretty extreme; Gwern, who wrote the pioneering analysis of this technology, was also skeptical. But Heliospect says they’ve got better predictors than academia that use the rare variants everyone else misses; after talking to the company, Gwern retracted his objections and says he finds their claim “pretty plausible”. Local ACX commenter geneticist Gene Smith also redid some calculations, changed his mind, and says “probably pretty realistic”. I find this interesting not just because of the polygenic selection angle, but because if Heliospect is right then their predictor is able to predict more genetic IQ than the “missing heritability” people believe exists, and it should be able to put this argument to bed once and for all. 53: This month in censorship: X/Twitter banned journalist Ken Klippenstein for sharing the Trump campaign’s dossier on JD Vance. Twitter’s side of the story is that the dossier was probably originally stolen by Iranian agents and they don’t want to support that kind of thing by letting people signal-boost the illicitly obtained goods; you can read Klippenstein’s side here. He appears to be unbanned now.

Inline links: if you Google “cool websites”, r/slatestarcodex, who works at the intersection of computer science and Talmud, Jerusalem Area House Party, says it’s a myth, Adderall decreases all-cause mortality in ADHD, some crazy mosques, https://substackcdn.com/image/fetch/$s_!2W0c!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc2be0549-aa2b-411d-b6a6-da50c34f7f86_1000x572.jpeg, Italy bans surrogacy, Italy’s tax code effectively bans startups?, Related, https://substackcdn.com/image/fetch/$s_!T3h-!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F08b011b3-ef20-42e5-91b2-33946b8ca7b6_1761x1355.jpeg, @cremieuxrecuel, responding to my critique of the cultural Christianity argument, My post, is finally going for, Trust Assembly, has an English-language Twitter account, has a new lab, wants to work on Alzheimers, and seems pretty based, not infinitely based, https://substackcdn.com/image/fetch/$s_!fY_K!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6b3b61c8-6c48-4eb7-9608-426b738ab9c8_592x111.png, which can use your computer, Claim, Ems Dispatch, The first use of artificial insemination in humans, administers several personality tests to the same people, https://substackcdn.com/image/fetch/$s_!epEc!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F47c474c6-d68a-41e4-954e-d24cd00ec040_740x388.webp, Whither Tartaria, a well-researched article showing, in a new article he demolishes a different theory, When Hamilton Lost Its Snob Appeal, Why are people such jerks to public intellectuals?, more, Some prominent Substack psychiatrists doing a video Q&A, submit your questions here, Naomi Kanakia, No, Intelligence Is Not Like Height, Family-GWAS Reveals Effects Of Environment And Mating On Genetic Associations, Howe 2022, a more specific post where he responds to Gusev’s points one by one, argues that the usual method of attacking twin studies is obsolete, goes into more depth about one of those ways, The Guardian spied on them and nonconsensually revealed their existence, discussion, previously argued, the pioneering analysis of this technology, Gwern retracted his objections, says, polygenic selection, banned journalist Ken Klippenstein, you can read Klippenstein’s side here., appears to be unbanned now

In Defense Of The Amyloid Hypothesis

August 14, 2025 · Original source

[18] N. Mattsson, S. Palmqvist, E. Stomrud, J. Vogel, and O. Hansson, “Staging β -Amyloid Pathology With Amyloid Positron Emission Tomography,” JAMA Neurology, vol. 76, no. 11, p. 1319, Nov. 2019, doi: 10.1001/jamaneurol.2019.2214.

Inline links: 10.1001/jamaneurol.2019.2214

[26] B. J. Hanseeuw et al., “Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study,” JAMA Neurology, vol. 76, no. 8, pp. 915–924, Aug. 2019, doi: 10.1001/jamaneurol.2019.1424.

Inline links: 10.1001/jamaneurol.2019.1424

[30] C. Xia et al., “Association of In Vivo [18F]AV-1451 Tau PET Imaging Results With Cortical Atrophy and Symptoms in Typical and Atypical Alzheimer Disease,” JAMA Neurology, vol. 74, no. 4, pp. 427–436, Apr. 2017, doi: 10.1001/jamaneurol.2016.5755.

Inline links: 10.1001/jamaneurol.2016.5755

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Astral Codex Ten

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