PREreviews of Tracking claim changes from preprint to publication across 72,644 biomedical studies using large language models

Tracking claim changes from preprint to publication across 72,644 biomedical studies using large language models

by Hao Yin, Woosung Ahn, Patrycja M. Forster, and Ruslan Rust

Posted: July 1, 2026
Server: bioRxiv
DOI: 10.64898/2026.06.30.735556

Abstract

Preprints disseminate biomedical research before peer review, but how much those scientific claims change before publication in journals remains uncertain. Here, we matched 72,644 bioRxiv preprints to their peer-reviewed publications by DOI and used a large language model (Claude Sonnet 4.6) to extract one primary and two secondary claims. Content change (unchanged, minor, major) and hedging shift (more cautious, more confident, unchanged) were compared. Four raters independently labelled a 550-pair subset; model–rater agreement (quadratic-weighted Cohen’s κ = 0.67) approached agreement among the raters themselves (κ = 0.76). The primary claims were unchanged in 39.8% of abstract pairs, minorly revised in 50.0%, and substantially revised in 10.2%. Claims became more cautious twice as often as more confident (8.4% vs 4.2%). Major revisions correlated with longer preprint-to-publication interval. Therefore, among bioRxiv preprints that later publish, central abstract claims remain stable through the preprint-to-publication transition.

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PREreview by Olavo Amaral

Authored by Olavo Amaral

I have reviewed this article for a journal and am posting the review here as a public contribution.

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PREreview by Ludo Waltman

Authored by Ludo Waltman

This is a review of version 2 of the paper ‘Tracking claim changes from preprint to publication across 72,644 biomedical studies using large language models’ posted on bioRxiv.

This paper presents a large-scale comparison between the version of an article posted on the bioRxiv preprint server and…

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