ArticleNPJ digital medicine2026
Feasibility and impact of a large language model pipeline for surgical trial abstracts.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abstracts of surgical randomised controlled trials (RCTs) are widely read but often omit key methodological details despite CONSORT guidance. We evaluated whether a tightly constrained large language model (LLM) pipeline could improve completeness and readability at scale. We conducted a three-phase in silico study of consecutive surgical RCTs indexed in PubMed (2005-2025) with open-access full texts in PubMed Central. A 14-item CONSORT-derived rubric (maximum 25 points) was developed and validated against expert scoring, demonstrating good agreement (concordance correlation coefficient 0.71, 95% CI 0.44-0.86) and high reproducibility (intraclass correlation coefficient 0.91, 95% CI 0.80-0.96). An automated pipeline using GPT-4o (OpenAI) generated rewritten abstracts from full texts under strict non-fabrication constraints. Among 651 RCTs, original abstracts showed low completeness (mean 9.06/25, 95% CI 8.58-9.53). Rewriting significantly improved completeness (mean increase 7.40 for 250-word and 8.06 for 300-word versions; both p < 0.0001), with gains across all CONSORT domains, particularly randomisation, harms, and trial registration. Readability improved slightly and correlated with completeness. A constrained LLM pipeline can substantially enhance the completeness of surgical RCT abstracts at scale, with potential applications in authoring, peer review, and editorial workflows, provided appropriate human oversight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.