I want more early vaccine data, actually, so that’s good.
There is a significant decrease in cancer rates among vaccinated compared to unvaccinated, but the early/late divide is less clear. If my statistics is up to snuff (no guarantee there), you can expect an error of ~sqrt(n) in discrete data where n is your count. With the late vaccines, this means an error in the cancer rate of about 2 because they saw ~4 cases (3.2 * 124,000/100,000 ≈ 4). If this is actually overestimating, we could see the rate as 2/124000 or 0.64/40000. In this case, you wouldn’t necessarily expect to see any cases in a sample of 40000.
So it’s not clear from this that early is better than late, though it certainly doesn’t suggest that it’s worse.
Deep learning doesn’t stop at llms. Honestly, language isn’t a great use case for them. They are—by nature—statistics machines, so if you have a fuck load of data to crunch, they can work very quickly to find patterns. The patterns might not always be correct, but if they are easy to check, then it might be faster to use them and modify the result compared to doing it all yourself.
I don’t know what this person does, though, and it will depend on the specifics of the situation for how they are used.