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Comment by Robert J. Millikin

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Researcher 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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Robert J. Millikin, 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 quoted sentence appears verbatim in the abstract of the preprint titled 'Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis' as mirrored by PMC/PubMed, and bioRxiv metadata match the same DOI/title. But the source is a 12-author paper (Bethany M. Moore, Jack Freeman, Robert J. Millikin, et al.), not a single-author statement by Robert J. Millikin alone, so this platform cannot verify it as a single-author quote; the record also shows version 1 was posted on 2026-06-07, not 2026-06-05. Direct fetch of the submitted bioRxiv full-text URL was blocked here. ([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 Robert J. Millikin