Tracking claim changes from preprint to publication across 72,644 biomedical studies using large language models
Year: 2026
Type: preprint
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 up to two secondary claims from their abstracts. Content change (unchanged, minor, major/substantial) 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 = 0.69) 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%). Substantial revisions correlated with longer preprint-to-publication interval. Therefore, among bioRxiv preprints that are later published, central abstract claims remain stable through the preprint-to-publication transition.
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7166840602