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(TLDR: Recent Cochrane review says zinc lozenges shave 0.5 to 4 days off of cold duration with low confidence, middling results for other endpoints. Some reason to think good lozenges are better than this.)

There's a 2024 Cochrane review on zinc lozenges for colds that's come out since LessWrong posts on the topic from 2019 [LW · GW], 2020 [LW · GW], and 2021 [LW · GW]. 34 studies, 17 of which are lozenges, 9/17 are gluconate and I assume most of the rest are acetate but they don't say. Not on sci-hub or Anna's Archive, so I'm just going off the abstract and summary here; would love a PDF if anyone has one.

• Dosing ranged between 45 and 276 mg/day, which lines up with 3-15 18mg lozenges per day: basically in the same ballpark as the recommendation on Life Extension's acetate lozenges (the rationalist favorite).
• Evidence for prevention is weak (partly bc fewer studies): they looked at risk of developing cold, rate of colds during followup, duration conditional on getting a cold, and global symptom severity. All but the last had CIs just barely overlapping "no effect" but leaning in the efficacious direction; even the optimistic ends of the CIs don't seem great, though.
• Evidence for treatment is OK: "there may be a reduction in the mean duration of the cold in days (MD ‐2.37, 95% CI ‐4.21 to ‐0.53; I² = 97%; 8 studies, 972 participants; low‐certainty evidence)". P(cold at end of followup) and global symptom severity look like basically noise and have few studies.

My not very informed takes:

• On the model of the podcast in the 2019 post, I should expect several of these studies to be using treatments I think are less or not at all efficacious, be less surprised by study-to-study variation, and increase my estimate of the effect size of using zinc acetate lozenges compared to anything else. Also maybe I worry that some of these studies didn't start zinc early enough? Ideally I could get the full PDF and they'll just have a table of (study, intervention type, effect size).
• Even with the caveats around some methods of absorption being worse than others, this seems rough for a theory in which zinc acetate taken early completely obliterates colds - the prevention numbers just don't look very good. (But maybe the prevention studies all used bad zinc?)
• I don't know what baseline cold duration is, but assuming it's something like a week, this lines up pretty well with the 33% decrease (40% for acetate) seen in this meta-analysis from 2013 if we assume effect sizes are dragged down by worse forms of zinc in the 2024 review.
  • But note these two reviews are probably looking at many of the same studies, so that's more of an indication that nothing damning has come out since 2013 rather than an independent datapoint.
• My current best guess for the efficacy of zinc acetate lozenges at 18mg every two waking hours from onset of any symptoms, as measured by "expected decrease in integral of cold symptom disutility", is:
  • 15% demolishes colds, like 0.2x disutility
  • 25% helps a lot, like 0.5x disutility
  • 35% helps some (or helps lots but only for a small subset of people or cases), like 0.75x disutility
  • 25% negligible difference from placebo

I woke up at 2am this morning with my throat feeling bad, started taking Life Extension peppermint flavored 18mg zinc acetate lozenges at noon, expecting to take 5ish lozenges per day for 3 days or while symptoms are worsening. My most recent cold before this was about 6 days: [mild throat tingle, bad, worse, bad, fair bit better, nearly symptomless, symptomless]. I'll follow up about how it goes!

Are those genuine flaws with the model, or is the terminology just suboptimal? Put another way, if you know someone's 5 factor conscientiousness and agreeableness scores, how useful is that for predicting their behavior?

The standard way to measure compute is FLOPS. Besides other problems, this measure has two major flaws: First, no one cares exactly how many FLOPS you have; we want to know the order of magnitude without having to incant "ten high". Second, it sounds cute, even though it's going to kill us.

I propose an alternative: Digital Orders Of Magnitude (per Second), or DOOM(S).

Yes. I argued that the causes for the malaise are over-determined, and medical liability and close-mindedness are both reasons. There are integrative and functional doctors who are more willing to prescribe off-label medications. But they are rare and I haven't researched them in depth yet. 

The causes for the malaise are over-determined but include among other reasons:  institutional inertia & red tape, lack of funding for repurposing generic drugs, regulations that often force companies to test only one new drug at a time against the standard of care (SoC), oncologists who refuse to deviate from SoC due to some combination of fear of medical liability and close-mindedness and coordination problems amongst pharmaceutical companies.

But if there were 10x more clinical trial results, there'd be a greater universe from which one could prescribe off-label medications, and the quality of off-label recommendations would be higher. And the FDA certainly is a blocker there.

Good to know. I also quoted your more detailed remark on AI Standards Lab at the top of this post.

I have made so many connections that have been instrumental to my research. 

I didn't know this yet, and glad to hear!  Thank you for the kind words, Nell.

Related question: What is the least impressive game current LLMs struggle with?

I’ve heard they’re pretty bad at Tic Tac Toe.

Maybe we can draw a line between the score an AI gets without using human written problem/solution pairs in any way, and the score an AI gets after using them in some way (RL on example questions, training on example solutions, etc.).

In the former case, we're interested in how well the AI can do a task as difficult as the test, all on its own. In the latter case, we're interested in how well the AI can do a task as difficult as the test, if working with humans training it for the task.

I really want to make it clear I'm not trying to badmouth o3, I think it is a very impressive model. I should've written my post better.

Progress in ML looks a lot like, we had a different setup with different data and a tweaked algorithm and did better on this task. If you want to put an asterisk on o3 because it trained in some specific way that's different from previous contenders, then basically every ML advance is going to have a similar asterisk. Seems like a lot of asterisking.

I'm not sure what the details would look like, but I'm pretty sure ASI would have enough new technologies to figure something out within 10,000 years.

I feel like this is the main load-bearing claim underlying the post, but it's barely argued for.

In some sense the sun is already "eating itself" by doing a fusion reaction, which will last for billions more years. So you're claiming that AI could eat the sun (at least) six orders of magnitude faster, which is not obvious to me.

I don't think my priors on that are very different from yours but the thing that would have made this post valuable for me is some object-level reason to upgrade my confidence in that.