craigdalton

Excuse the blunt metaphor, but there is a risk here of turning on a fire-hose of "fresh" garbage. John Ioannidis, one of the doyens of evidence based medicine very persuasively argues - Why Most Published Research Findings Are False https://pmc.ncbi.nlm.nih.gov/articles/PMC1182327/ That is why platforms pay physicians/epidemiologists/ specialists in their field hundreds of dollars per hour to sort the good from bad papers. After my training as a doctor I did a Masters in Clinical Epidemiology and spent an afternoon each week in a tutorial that reviewed papers in the top journals - about 20-30% of them had major flaws that were either ignored or dismissed by the authors. It may be worse now. LLMs still have trouble picking up the subtleties of medical science and will miss papers with major flaws. I just did a test on a paper that is often quoted as providing evidence of excess cancer risk in communities living close to unconventional gas facilities. When I asked ChatGPT 5.2 to review the pape for evidence of increase cancer risk with a simple prompt it said the paper found such a risk. However, when I wrote a multi-discipline based prompt for 5.2 and Gemini 3 pro, it found the fatal flaw in the paper and advised it did not provide evidence. See the prompt and consider how the prompts would have to be individually developed for each paper and meta-analysis.

For review of meta-analysis you would need prompts developed by expert methodologists and discipline specialists- here is the prompt that worked: You are an environmental epidemiologist and exposure scientist, critially review this papers claim that the measured levels of unconventional gas emissions provide evidence of excess cancer risk: https://link.springer.com/article/10.1186/1476-069X-13-82

There are well established hierarchies of evidence in health studies and this study would rank as low quality evidence due to inability to control for other factors. See https://gdt.gradepro.org/app/handbook/handbook.html#h.3183vu... for background.

In studies of pollution and impacts on health, the confounding factors often have a larger impact on the health outcome than the pollutant, such as particulate level, and therefore significant control of confounders is required to estimate any impact on the health outcome. The strong effect in this study is highly suggestive of a confounder rather than a real effect from particles or other pollutants and therefore would require a much better study design to support tacking action at a policy level with an expectation of a huge impact.

Fine to put filters into improve overall air quality but just not with the benefit rationale suggested in this study.