Astral Codex Ten

Strongyloides

28 min read

Strongyloides

Article

Strongyloides is a recurring concept in the Astral Codex Ten archive, appearing 4 times across 4 issues between November 17, 2021 and February 14, 2023. The archive places it in contexts such as “areas with low Strongyloides prevalence”; “for it to make even the slightest dent in the numbers, you would have to assume very very high prevalence of Strongyloides”; “a common parasitic worm called Strongyloides”. It most often appears alongside ivermectin, Alexandros Marinos, Carvallo.

Metadata

  • Category: Concepts
  • Mention count: 4
  • Issue count: 4
  • First seen: November 17, 2021
  • Last seen: February 14, 2023

Appears In

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.

Ivermectin: Much More Than You Wanted To Know
November 17, 2021 · Original source
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.
Inline links: source, Gluchowska et al, the WHO, carries, https://substackcdn.com/image/fetch/$s_!xExE!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5da21781-249c-4e59-b616-9f23d83cc044_2048x1184.jpeg, https://substackcdn.com/image/fetch/$s_!4SMr!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fdcd6e4b2-37f7-4602-93d5-2581c3b27a60_700x432.png, https://substackcdn.com/image/fetch/$s_!-6n2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F7fd6e8f4-093e-4e02-bce7-363615146c9c_2228x1346.jpeg, https://substackcdn.com/image/fetch/$s_!CPZs!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb0425847-198a-4bd3-a63b-149f15d147ba_700x432.png, https://substackcdn.com/image/fetch/$s_!H3rK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F9972491b-25b0-4c06-8aca-86fce102ae63_666x147.png, even in 2014, The Carlisle-Stouffer-Fisher method
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.
Inline links: https://substackcdn.com/image/fetch/$s_!H3rK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F9972491b-25b0-4c06-8aca-86fce102ae63_666x147.png, even in 2014, The Carlisle-Stouffer-Fisher method
Highlights From The Comments On Ivermectin
November 23, 2021 · Original source
The more realistic stance though is that death or worsening due to hyperinfection is a rather rare outcome and doesn't influence numbers significantly. That's why the doctors in those countries went along with a study that would otherwise be unethical. I still don't know where the significance comes from, but it's not strongyloides hyperinfection.
Let’s start with the negative comments. Leading pro-ivermectin website ivmmeta.com understandably disagreed with my fisking of them. They have a section where they respond to critics (see responses to Gideon Meyerowitz-Katz, to the BBC, to the parasitic worm hypothesis, and to someone named AT who they won’t explain further). I was honored to also get a response here. They write:
Inline links: Gideon Meyerowitz-Katz, the BBC, the parasitic worm hypothesis, someone named AT who they won’t explain further
The steps required to accept the strongyloides-mechanism-only conclusion are also extreme - we need to disregard the majority of outcomes occuring before steroid use, and disregard the strong treatment-delay response relationship which is contradictory. Figure 24 shows analysis by strongyloides prevalence.
Inline links: Figure 24
Response To Alexandros Contra Me On Ivermectin
February 01, 2023 · Original source
I thought the most plausible explanation for the discrepancy was Dr. Avi Bitterman’s hypothesis (now written up here) that ivermectin worked for its official indication of treating parasitic worms. COVID is frequently treated with steroids, steroids prevent the immune system from fighting a common parasitic worm called Strongyloides, and sometimes people getting treated for COVID died of Strongyloides hyperinfection. Ivermectin could prevent these deaths, which would mean fewer deaths in the treatment group than the control group, which would look like ivermectin preventing deaths from COVID in high-parasite-load areas (like the tropics) but not low-parasite-load areas (like temperate zones). This explained some of the mortality results, with the other endpoints likely being because of publication bias.
Inline links: here
“Synthetic control groups” - ie comparing people in a trial to some previously-known understanding of how a disease progresses - are a standard practice, and basically fine. Borody et al indeed have had amazing careers with many things they can be proud of. But I continue to believe that this paper is not among them. Synthetic control groups are more common in social sciences, but have occasionally been used in pharmacology when it would be unethical or extremely difficult to use a real control group. The most common use case is rare cancers, where it takes years to get enough patients to test a drug and it also seems kind of unethical to delay. Another good thing about rare cancers is that they're pretty discrete; you don't have to worry about things like "well, 90% of leukemias never make it to a doctor anyway, so maybe we're only seeing the serious leukemias" or "these guys counted the leukemias that get dealt with by the local doctors' office, but those other guys counted the leukemias that have to go to the hospital". More important, studies with synthetic control groups usually go above and beyond to justify why their synthetic control group should be a fair comparison to the treatment group. Here's an example, from a paper about a rare leukemia. They start by getting a synthetic control group from a previous randomized controlled trial of leukemia drugs (not the general population!) Then they throw out more than half their patients for not being a good match for the selection criteria of the current study. Then they investigate whether there are significant differences on five important demographic factors, and find a few. Then they re-weight the patients in the historical comaprator study to adjust out the differences between the previous population and the current population. Then they do some analyses to check if they re-weighted everything correctly. Then they apologize profusely for having to use this vastly inferior methodology at all: In special cases when a disease is rare, prognosis is very poor, and there are limited therapeutic options available, single-arm clinical trials may be used as evidence for accelerated drug approvals. Comprehensive evaluation of historical comparator or reference data can provide an additional approach for putting the efficacy of a new therapy into perspective.11, 12 In this study, we applied different statistical methods and sensitivity analyses to evaluate the clinical efficacy of blinatumomab against historical data. Concerns often raised regarding the use of historical comparator data are the influence of potential biases related to selection, misclassification and confounding.12 The requirement of rigorous eligibility criteria in the blinatumomab clinical study—such as Eastern Cooperative Oncology Group status of two or lower and absence of abnormal lab values during screening—may increase the chance of better outcomes in the clinical study than the historical data. While it may be possible to use unadjusted historical data when patient populations are sufficiently similar,27 the disproportionate number of advanced-stage patients in the blinatumomab trial required methods applied to individual-level data to minimize bias. Selection bias was minimized by use of stringent inclusion criteria into the historical data set and by weighting or adjusting for known prognostic factors. In addition, the historical data set represented adult R/R patients who received standard of care (excluding palliative care patients where possible), without any restrictions to any patient subgroups. Residual confounding may still remain and be difficult to control for, particularly in data sets where differences in important prognostic factors are unknown or not measured in one data set. In this study, nearly all known important prognostic factors were adjusted for in the weighted or propensity score analyses. Missing data on key covariates lead to exclusion of some records from the analyses (Figure 1), which may theoretically bias the overall results. However, our examination of records with missing covariates did not identify significant differences by patient demographic characteristics compared with patients who had complete data (data not shown). Misclassification bias was limited by harmonization of patient-level data in the pooled analysis, which employed common data definitions for disease classification and outcomes characterization. Compare this to how the Borody study discusses its synthetic control group: The control data was from contemporary infected subjects in Australia obtained from published Covid Tracking Data. I hesitate to say “they didn’t even say which tracking data”, because in the past I’ve said things like that and just missed it. But I can’t find them saying which tracking data. In Borody et al’s synthetic control group, 70/600 (11.5%) patients required hospitalization. But the US hospitalization rate appears to be about 1% for unvaccinated individuals. So Borody’s synthetic control group got 10x the expected hospitalization rate. This seems very relevant to this study finding that ivermectin decreases hospitalization by 90%! I’m not claiming this is fraudulent, or impossible, or means the study couldn’t have been good. And Borody claim to have used an “equivalent” control group, so maybe there was some adjustment done for this. But this is why we usually use more than one word to describe our control groups! Or use real control groups that don’t ruin your study if you do a finicky adjustment slightly wrong! I feel like these are the kinds of questions Alexandros needs to be asking, instead of just giving a link to a Stat News article about how sometimes synthetic control groups are okay. Also other questions, like “how come this found a 90% decrease in hospitalization and mortality, but lots of other studies found smaller decreases, and the biggest and best studies found none at all?” I know Alexandros’ answers are to find lots of flaws with the biggest and best studies, but these flaws wouldn’t be enough to cover up a 90% cure rate. And if you’re in the business of calling out flaws in studies I genuinely think having your control group be “we used some group of people somewhere in Australia, they had 10x the normal hospitalization rate, we won’t tell you anything else” would be the sort of flaw you would call out! Thomas Borody is a genuinely brilliant gastroenterologist and I am very grateful for his life-saving discoveries. But Elon Musk is a genuinely brilliant engineer and I am very grateful for his low-cost reusable rockets - and this doesn’t mean he never does crazy inexplicable things. Maybe Borody and his collaborators have a point from this study, but I don’t feel like it makes sense as written. If they ever explain what they were doing in more detail and it’s some sort of amazing 4D-chess move that makes total sense, I will apologize to them. Otherwise, stick to inventing amazing life-saving digestive therapies. In response to this section, Alexandros stresses that he is not necessarily saying Borody et al is incorrect or challenging my decision to leave it out. He writes: I will repeat that my strong objection, is that you wrote " this is not how you control group, @#!% you". I therefore pointed to stat news to support my case that, yes, this can indeed be how you control group. That's all. In the article I even noted that this aversion towards disrespect to elders may even be a cultural difference between us. To be clear, if I were making a case for ivermectin, I would not be relying on this study as my starting point. III. Hokey Meta-Analysis Alexandros points out that I used the wrong statistical test when analyzing the overall picture gleaned from this studies. He’s right. The right statistical test would make ivermectin look stronger, without changing the sign of the conclusion. After getting a core group of potentially trustworthy studies, I tried to see whether ivermectin still had a statistically significant positive effect in them. I tried to be honest that I didn’t really know how to do formal meta-analyses: 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 . . . What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? . . . Now it’s p = 0.04, seemingly significant I in fact could not do simple summary statistics to this. Alexandros describes the test I should have used, a DerSimonian-Laird test, and applies it to the same data. Now the numbers are p = 0.03 and p < 0.0001. I accept that I was wrong, he is right, and this is more accurate. My original conclusion to this section is that although you couldn’t be absolutely sure from the numbers, eyeballing things it definitely looked like ivermectin had an effect. I then went on to try to explain that effect. With Marinos’ corrections, you can be sure from the numbers, but the rest of the post - an attempt to explain the effect - still stands. IV. Worms Alexandros brings up issues with the Strongyloides hypothesis; Dr. Bitterman graciously responds. I find the issues real enough to lower my credence in the idea, but not to completely rule it out. Even if it is true, I probably overestimated how important it was. My original explanation for the effect was Dr. Avi Bitterman’s theory of Strongyloides hyperinfection. Many people in certain tropical regions are infected with the parasitic worm Strongyloides. Usually a person’s immune system keeps this worm under control, and the parasites cause only limited problems. But under certain situations - especially when people take immune-suppressing corticosteroids - the immune system fails, the worms multiply, and the patient can potentially die of sudden worm overgrowth (“hyperinfection”). Corticosteroids are a common COVID treatment. So plausibly some people in tropical areas fighting COVID are at risk of dying from worm hyperinfection. Ivermectin was originally an anti-parasitic-worm medication before being repurposed to fight COVID, and everyone agrees it is very good at this. So if many people in COVID trials are dying of worm infections, then ivermectin could help them. This would look like ivermectin reducing mortality in COVID trials, and make people wrongly conclude that ivermectin treats COVID. Alexandros responds to this theory here, again I’ll try to summarize: The original Bitterman paper concludes that ivermectin trials show stronger results in high-Strongyloides-prevalence regions. But it mixes prevalence data from two different papers with different methodologies. Correcting for this, the findings no longer clear a formal bar for statistical significance, and don’t really look significant either.
Inline links: are a standard practice, Here's an example, 11, 12, 27, Figure 1, about 1% for unvaccinated individuals, here
The original Bitterman paper concludes that ivermectin trials show stronger results in high-Strongyloides-prevalence regions. But it mixes prevalence data from two different papers with different methodologies. Correcting for this, the findings no longer clear a formal bar for statistical significance, and don’t really look significant either.
Contra Kavanagh On Fideism
February 14, 2023 · Original source
A picture my instructor took of me at one of the ruins. Nobody was under any obligation to handhold me out of my Atlantis beliefs. But the #1 Google rank for “site about how Atlantis isn’t real” is a scarce resource. Article space on skeptic blogs (podcasts were still years into the dystopian future at this point) was a scarce resource. And when people frittered these scarce resources away on a thousand identical pieces saying “lol you’re stupid and racist if you believe this, haven’t you heard that conspiracies are always wrong?” - and never on any explanation of the GIANT UNDERWATER PYRAMIDS - yes, I feel like I was wronged. Eventually I lifted myself up by my own bootstraps. I studied some of the relevant history myself (less impressive than it sounds, Wikipedia was just coming into existence around this time). I learned enough about geology to understand on a gut level how natural processes can sometimes produce rocks that are really really artificial-looking - yes, even as artificial-looking as the ones in the picture above. More important, I learned something like rationality. I learned how to make arguments like the one I use in The Pyramid And The Garden. I realized that, for all their skill at finding anomalies, the Atlantis books couldn’t agree on a coherent narrative of their own. Some placed Atlantis in the Atlantic, others in the Pacific, others in Antarctica; some used it to explain artifacts from long after others said that it fell. For a while if I squinted I could sort of kind of smush them into a single story, but that story had even more anomalies than normal historians’. Eventually I gave up and joined the mainstream. I’m not angry at Graham Hancock. I see no evidence he has ever been anything but a weird, well-meaning guy who likes pyramids a little too much. But I feel a burning anger against anti-conspiracy bloggers, anti-conspiracy podcasters, and everyone else who wrote “lol imagine how stupid you would have to be to believe in Atlantis” style articles. Either these people didn’t understand the arguments for and against Atlantis, or they did. If they didn’t, they were frauds, claiming expertise in a subject they knew nothing about. If they did, then at any moment they could have saved me from a five year wild-goose-chase - but chose not to, because it was more fun to insult me. II. Kavanagh makes fun of me for writing 25,000 words on ivermectin. I agree this might not have been the best use of my time, and I would accept this criticism from anyone except Kavanagh - who’s devoted his whole career to thinking about ivermectin and ideas closely aligned to it. There’s a Hindu legend (maybe apocryphal?) about an atheist philosopher who spends literally every second of every day denouncing God. When he dies, God welcomes him into the highest heaven, praising him as a great yogi - for he never let his consciousness stray from awareness of God even for one moment. If by some inexplicable theological anomaly Bret Weinstein turns out to be God, Chris Kavanagh is definitely going to the highest heaven. So Kavanagh’s complaint can’t be that I’m thinking about this question at all. He sort of hints at a complaint where it took me too long to figure out that ivermectin didn’t work - shouldn’t I have been able to do it without the long review? But I clearly said on my first post on the subject that I had long since decided it to my own satisfaction, and was just trying to clear up some of my remaining questions. What is his complaint? At the risk of putting words in his mouth, two parts of his comment stand out to me as having important arguments: I interpret this as - to even try to “evaluate the evidence” at all is a mistake, because it suggests there might be evidence on both sides. Instead, you should admit that some people are idiots who believe things there’s no evidence for, and move on. But the problem with “if studies had supported ivermectin as an effective treatment, it would have been adopted”, is that about thirty different studies did support it, and it was adopted in several countries, mostly in Latin America. The first few meta-analyses of ivermectin found that it worked! I’m not defending ivermectin here. I think there was a reasonable explanation of this: a combination of fraud, poor methodology, publication bias, and maybe Strongyloides infections. But until someone tells you the reasonable explanation, there’s no reasonable explanation! It’s like the giant underwater pyramids. If I go diving and see the giant underwater pyramids, and you just say “LOL, you are stupid, don’t you know conspiracy theories aren’t real?”, you’re not going to convince me! I wanted to give the reasonable explanation, in terms that people could understand. Before doing any research, I had some intuitive guesses about what the reasonable explanation would look like - something something methodological problems something something small studies. But this, itself, isn’t a reasonable explanation. It’s an IOU for a reasonable explanation. I agree that many people are unreasonable and don’t respond to reasonable explanations. I think sometimes this is genetic or something and can’t be helped, but other times it comes after a hundred different experiences where you want reasonable explanations and don’t get them and also people are jerks to you and you learn that the establishment can’t be trusted. Mahabharata: “Even after ten thousand explanations, the fool is no wiser, but the wise man requires only two thousand five hundred.” If I had had to suffer through a few more skeptics calling me racist because I wanted to know why there were giant underwater pyramids, I probably would have believed in Atlantis even harder, out of spite, and never talked myself out of it. And then when ivermectin came along, I would have thought “Scientists? Experts? They’re the guys who are so dumb they can’t even figure out Atlantis existed when there are giant underwater pyramids right in front of their eyes. Screw them, I’m listening to Bret Weinstein.” I side with the Christians. There may be people so far gone into the outer darkness that they can’t be saved, but you are forbidden from ever believing with certainty that any specific individual is in this category. Act as if everyone is one good deed away from falling to their knees and acknowledging the light of Jesus. Moving on: @RachelBCam Imagine Scott’s blog with some more generous degrees of freedom exercised in his analysis, suddenly you have a more positive result &amp; the impression the issue is a genuine controversy. Indeed, this is what people like Alexandros did in response.","username":"C_Kavanagh","name":"Chris Kavanagh","profile_image_url":"","date":"Tue Feb 14 15:50:05 +0000 2023","photos":[],"quoted_tweet":{},"reply_count":0,"retweet_count":0,"like_count":0,"impression_count":0,"expanded_url":{},"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM"> This is the part I have the most trouble interpreting charitably. I can’t stop reading it as “doing good science is a near occasion of sin for doing bad science”. It sounds kind of like fideism, the belief (more common in atheists’ imaginations than real religion) that somebody who reasons their way to belief in God is a sinner, because a real saint would have believed through blind faith, without having to reason. The best I can do is to think of this as a PR argument: it looks bad to be treating these kinds of questions as live issues. I generally don’t like PR arguments, but while we’re having them: doesn’t it kind of look bad for one side to be promoting fideism? The ivermectinist slogan is “do your own research”. Kavanagh’s apparent slogan is “don’t do research” - even if you get it right, having tried it at all makes you impure. If there’s some argument I know nothing about - pro- vs. anti- skub, perhaps - and all I’ve heard is that the pro-skub people say that you should look at evidence and decide rationally based on your best judgment, and the anti-skub people say you should never look at evidence and have to trust them - I’m already 90-something percent sure pro-skub are the good guys. My model of the PR here - of the overall milieu and psychological factors that turn people into conspiracists - is that they spot some giant underwater pyramids, compelling-seeming facts that appear to point toward conspiracy. These facts have alternative explanations, but these alternatives are less compelling and harder to explain. Realistically some people are going to get caught up in the conspiracy’s superior first-level compellingness and you can’t help them. But other people are on the fence and can be talked down. This is the job of the pro-mainstream-anti-conspiracy people. Instead of doing their job, these people: ignore them
Inline links: The Pyramid And The Garden, so far gone into the outer darkness that they can’t be saved, pro- vs. anti- skub