ArticleBMC medical research methodology2026
Assessing data extraction in randomized clinical trials with large language models.
Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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9 authors.
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Abstract
backgroundData extraction is an essential step in evidence synthesis but remains time-consuming and prone to human error. Large language models (LLMs) such as ChatGPT-4 and Claude 3 Opus may offer partial automation solutions. This proof-of-concept study evaluated their preliminary performance in extracting data from full-text randomized controlled trial (RCT) reports within systematic reviews.
methodsTwo previously validated systematic reviews published in European Urology (105 trials in total) were selected. Standardized prompts were created and optimized with ChatGPT-4 and tested independently across trials using both ChatGPT-4 (paid version) and Claude 3 Opus via the standard web interface. Each prompt was executed three times, and only the first output was used to calculate the proportion of correctly extracted items (Pacc). Extracted data were compared with verified gold-standard datasets.
resultsFor binary outcomes, ChatGPT-4 and Claude 3 Opus showed high accuracy in group size extraction (Pacc: 91%-94%) and moderate accuracy for event counts (Pacc: 57%-71%). For continuous outcomes, group size accuracy was moderate (Pacc: 59%-69%), while mean and standard deviation extraction was poor (Pacc: 24%-56%). Test-retest reliability was substantial to almost perfect.
conclusionsCurrent LLMs can assist in automating data extraction for binary outcomes but remain inconsistent for continuous outcomes. These preliminary results should be interpreted with caution. Further research using larger datasets and iterative prompt refinement is needed before LLMs can be reliably integrated into systematic review workflows.
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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.