ArticleClinical and experimental emergency medicine2026
Automated chain-of-thought evaluation framework for large language model-generated emergency department documentation: a simulation-based study.
Article in Clinical and experimental emergency 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate MEDIVAL (Medical Documentation Validation), a progressive chain-of-thought (CoT) evaluation framework for automated assessment of large language model (LLM)-generated emergency department documentation, designed to align with expert clinical judgment in acute care settings.
methodsWe designed a three-tier evaluation framework incorporating persona-based, error-enhanced, and insight-integrated strategies. The framework was tested across four LLMs (GPT-4o, GPT-4.1, Claude-3.5, Claude-3.7) on 33 emergency department records reviewed by four expert emergency physicians. Each model applied the three CoT strategies across five criteria: appropriateness, accuracy, structure/format, conciseness, and clinical validity. Model outputs were compared with expert ratings using Spearman correlation coefficients. Differences were analyzed with the Friedman test and Wilcoxon signed rank test with Bonferroni correction. Reproducibility was assessed through intraclass correlation coefficient (ICC) analysis.
resultsAll models demonstrated stronger alignment with expert ratings as CoT complexity increased, with Claude-3.7 (r=0.712, P<0.001) and GPT-4o (r=0.702, P<0.001) showing the highest correlations under the insight-integrated strategy. GPT-4.1 showed the greatest relative improvement (43.3% increase, r=0.457 to r=0.655, P<0.001). Significant overall differences were observed across strategies (χ2 (2)=48.39, P<0.001), though the error-enhanced and insight-integrated approaches differed only modestly yet significantly (P=0.002). High reproducibility was confirmed (ICC >0.919), with Claude-3.5 achieving the most consistent results (ICC, 0.997-0.998).
conclusionMEDIVAL demonstrates that progressive CoT strategies systematically improve automated evaluation of emergency department documentation while maintaining excellent reproducibility. This framework offers a viable prescreening tool to reduce expert workload and support reliable artificial intelligence integration into emergency medicine workflows.
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.