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Comment by John P. A. Ioannidis

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Physician-scientist and meta-researcher at Stanford University, known for work on the reliability of scientific research
These implausible estimates of benefits or risks associated with diet probably reflect almost exclusively the magnitude of the cumulative biases in this type of research, with extensive residual confounding and selective reporting.
AI Verified (Aug 23, 2018)
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John P. A. Ioannidis, Stanford, JAMA Viewpoint, "The Challenge of Reforming Nutritional Epidemiologic Research"
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Observational nutrition studies are too confounded to show whether eggs cause harm
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AI Verified In source context, Ioannidis explicitly discusses an observational finding that eating 1 egg daily would cut life expectancy, then argues that such diet-risk estimates largely reflect cumulative bias, residual confounding, and inadequate cohort data for causal inference. So even though the stored quote is general, it is directly tied to whether observational nutrition studies can show egg harm and gives a determinable stance signal. ([statmodeling.stat.columbia.edu](https://statmodeling.stat.columbia.edu/wp-content/uploads/2018/08/jama_Ioannidis_2018_vp_180095.pdf)) · YouCongress gpt-5.4-2026-03-05 · 1h ago
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AI Verified The quote strongly supports the statement: Ioannidis says the reported diet "benefits or risks" in this research type likely reflect "extensive residual confounding and selective reporting" rather than true causal effects. In context, the article is criticizing nutritional epidemiology for treating associations as causal, so the inference to foods such as eggs is strong even though eggs are not named in the quote. ([jamanetwork.com](https://jamanetwork.com/journals/jama/fullarticle/2698337)) · YouCongress gpt-5.4-2026-03-05 · 1h ago

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AI Verified ai_verified · YouCongress gpt-5.4-2026-03-05 · 1h ago
replying to John P. A. Ioannidis