SynthesisJAMA network open2025
Large Language Model Analysis of Reporting Quality of Randomized Clinical Trial Articles: A Systematic Review.
Synthesis in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- Integration of large language models and evidence-based Chinese medicine: A scoping review.Integrative medicine research · 2026Review
- SPIRIT-CONSORT-ELM: Element-Level Annotated Dataset and Large Language Model Approach for Assessing Randomized Controlled Trial Reporting.medRxiv : the preprint server for health sciences · 2026Article
- Generative AI and Clinicians Show Comparable Prognostic Reasoning From Clinical Narratives in Biologic-Treated CRSwNP.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Feasibility and impact of a large language model pipeline for surgical trial abstracts.NPJ digital medicine · 2026Article
- [Application and Reflection of Artificial Intelligence in Pharmacy Education].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Comparative evaluation of large language models for guideline-compliant abstract generation and readability in dental research: an experimental comparative study.Scientific reports · 2026Article
- The Evolving Role of Large Language Models in Health Care Research and Scientific Communication.Mayo Clinic proceedings · 2026Article
- Completeness of reporting in abstracts of randomized controlled trials and factors associated with complete reporting: a meta-research study.Research integrity and peer review · 2026Article
- Variability among large language models in assessing CONSORT compliance of published randomized clinical trials.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Incomplete reporting in randomized clinical trials (RCTs) obscures bias and limits reproducibility. Manual audits for adherence to the Consolidated Standards of Reporting Trials (CONSORT) guideline cannot keep pace with publication volume. Objectives: To build and validate a zero-shot large-language-model (LLM) pipeline for automated CONSORT assessment and to map reporting quality over time, biomedical disciplines, and trial features. Design, Setting, and Participants: This systematic review included RCTs that were indexed on PubMed, available in English, open access, human-participant research, and published between MONTH 1966 to MONTH 2024. PubMed PDFs were converted to XML and linked with Semantic Scholar and ClinicalTrials.gov metadata. Chat GPT-4o-mini was tested on the 50-article CONSORT-Text Classification Model (CONSORT-TM) benchmark, checked by experts in 70 randomly sampled RCTs, and then applied to the full sample. Exposure: Publication year, biomedical discipline, funding source, trial phase, US Food and Drug Administration regulation, and oversight features. Main Outcomes and Measures: The LLM judged whether each of 21 CONSORT items was met. Primary outcomes were (1) model performance vs expert review (precision, recall, and macro F1 score) and (2) proportion of items reported. Results: Of 53 137 screened PDFs, 21 041 RCTs (median [IQR] publication year, 2014 [2003-2020]; 30 disciplines) were included, with a registry-linked subset of 1790 RCTs that had a median (IQR) planned enrollment of 210 (95-440) participants. In the 70-article validation set (2210 decisions) LLM outputs matched experts 91.7% of the time (2026 of 2210 decision); the macro F1 score on CONSORT-TM was 0.86 (95% CI, 0.84-0.87). Mean CONSORT compliance increased from 27.3% (95% CI, 27.0%-27.6%) in 1966 to 1990 to 57.0% (95% CI, 56.8%-57.2%) in 2010 to 2024. However, reporting critical elements remained uncommon, such as allocation-concealment mechanism (16.1% [95% CI, 15.6%-16.6%]) and external-validity discussion (1.6% [95% CI, 1.5%-1.8%]). Compliance varied across disciplines from 35.2% (95% CI, 34.8%-35.6%) in pharmacology to 63.4% (95% CI, 62.1%-64.7%) in urology and showed only negligible associations with clinical trial characteristics (all Cramer V <0.10). Conclusions and Relevance: In this systemic review of RCTs, a zero-shot LLM audited CONSORT adherence at scale, uncovering persistent reporting gaps and wide disciplinary variation across biomedical fields, underscoring the need for targeted editorial action to boost transparency and reproducibility.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.