ArticleBMC medical research methodology2024
Concordance between humans and GPT-4 in appraising the methodological quality of case reports and case series using the Murad tool.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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.
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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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative artificial intelligence use in evidence synthesis: A systematic review.Research synthesis methods · 2025Pooled it
- Using full agreement across multiple large language models for title-and-abstract screening in systematic reviews: a proof-of-concept.Systematic reviews · 2026Article
- Not Ready for Prime Time: Limitations of a Retrieval-Augmented Generation Large Language Model in Assessing Risk of Bias in Observational Studies.Journal of the Pediatric Orthopaedic Society of North America · 2026Article
- Human-AI collaboration enhances the performance of large language models in risk of bias assessment.BMC medical research methodology · 2026Article
- Comparison of three large language models' ability to assess the risk of bias using ROBINS-I tool.BMJ digital health & AI · 2026Article
- Leveraging AI for Meta-Analysis: Evaluating LLMs in Detecting Publication Bias for Next-Generation Evidence Synthesis.Cochrane evidence synthesis and methods · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAssessing the methodological quality of case reports and case series is challenging due to human judgment variability and time constraints. We evaluated the agreement in judgments between human reviewers and GPT-4 when applying a standard methodological quality assessment tool designed for case reports and series.
methodsWe searched Scopus for systematic reviews published in 2023-2024 that cited the appraisal tool by Murad et al. A GPT-4 based agent was developed to assess the methodological quality using the 8 signaling questions of the tool. Observed agreement and agreement coefficient were estimated comparing published judgments of human reviewers to GPT-4 assessment.
resultsWe included 797 case reports and series. The observed agreement ranged between 41.91% and 80.93% across the eight questions (agreement coefficient ranged from 25.39 to 79.72%). The lowest agreement was noted in the first signaling question about selection of cases. The agreement was similar in articles published in journals with impact factor < 5 vs. ≥ 5, and when excluding systematic reviews that did not use 3 causality questions. Repeating the analysis using the same prompts demonstrated high agreement between the two GPT-4 attempts except for the first question about selection of cases.
conclusionsThe study demonstrates a moderate agreement between GPT-4 and human reviewers in assessing the methodological quality of case series and reports using the Murad tool. The current performance of GPT-4 seems promising but unlikely to be sufficient for the rigor of a systematic review and pairing the model with a human reviewer is required.
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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.