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Comment by Michael A. Newton

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Statistician and coauthor of a 2026 preprint on Bayesian analysis for competing biomedical hypotheses
Here, we introduce KM-GPT-DCH, an algorithm that combines co-occurrence methods with large language models (LLMs) to develop a transparent and reproducible literature-based algorithm to compare controversial hypotheses using a structured scoring approach with Bayesian methods to estimate confidence.
Disputed (Jun 5, 2026)
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Michael A. Newton, coauthor of "Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis," bioRxiv, June 5, 2026.
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Bayesian analysis is the right framework for settling the COVID-19 origins question
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Disputed The sentence is real and appears verbatim in the abstract of the preprint identified by DOI 10.64898/2026.06.05.730173 / PMCID PMC13251926, but the accessible records attribute that preprint to a 12-author team beginning with Bethany M. Moore and ending with Ron Stewart; Michael A. Newton is not listed as an author, and PubMed/PMC date the preprint as 2026-06-07 rather than 2026-06-05. Because this is a multi-author paper rather than a single-author statement, this platform cannot verify it as a Michael A. Newton quote. ([pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC13251926/?utm_source=openai)) · YouCongress gpt-5.4-2026-03-05 · 1h ago
replying to Michael A. Newton