A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial

Automated adjudication of clinical outcomes from telephone follow-ups is crucial for reducing workload and increasing data quality in large-scale trials. Here, we show that a domain-specific large language model (Fu-LLM) effectively automates the preadjudication of key clinical events-including death, hospitalization, and medication use-based on 1,046 vignettes of follow-up telephone interviews conducted across three centers in a randomized clinical trial (China CT-FFR Study 3). Fu-LLM outperforms not only state-of-the-art general-purpose LLMs (e.g. GPT-3.5-turbo, GPT-4o, DeepSeek-v3, Claude 3.5-Sonnet, and Gemini-2.0-Pro) and conventional machine learning models (Support Vector Machine), but also human adjudicators in a silico human-model comparative study. It also shows greater robustness than different versions of GPT-4 do in temporal drift tests. Our findings demonstrate that Fu-LLM can significantly streamline outcome identification in clinical trials, offering a scalable and accurate tool for automating labour-intensive adjudication processes.

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Publication Details

Journal
Nature Communications
Published
2025-12-01
DOI
https://doi.org/10.1038/s41467-025-66910-6
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
2.48

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article

A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial

Qinmei Xu, Fandong Zhang, Long Jiang Zhang, Jian Zhong et al.
1 citations
Nature Communications
Machine Learning in Healthcare
2.48
article

A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial

Qinmei Xu, Fandong Zhang, Long Jiang Zhang, Jian Zhong, Bangjun Guo, Chun Yang, Han Wang, Zijian Chen, Jinlong Zhao, Bin Hu, Zhao Shi, Zezhong Li, Jie-Hong Xu, Huimin Pang, Yizhou Yu, Bingqian Wu, Yueyan Wang
article en
1 citations

Abstract

Automated adjudication of clinical outcomes from telephone follow-ups is crucial for reducing workload and increasing data quality in large-scale trials. Here, we show that a domain-specific large language model (Fu-LLM) effectively automates the preadjudication of key clinical events-including death, hospitalization, and medication use-based on 1,046 vignettes of follow-up telephone interviews conducted across three centers in a randomized clinical trial (China CT-FFR Study 3). Fu-LLM outperforms not only state-of-the-art general-purpose LLMs (e.g. GPT-3.5-turbo, GPT-4o, DeepSeek-v3, Claude 3.5-Sonnet, and Gemini-2.0-Pro) and conventional machine learning models (Support Vector Machine), but also human adjudicators in a silico human-model comparative study. It also shows greater robustness than different versions of GPT-4 do in temporal drift tests. Our findings demonstrate that Fu-LLM can significantly streamline outcome identification in clinical trials, offering a scalable and accurate tool for automating labour-intensive adjudication processes.

Nature CommunicationsVol. 17(1)
Nanjing General Hospital of Nanjing Military Command (CN), Beijing Enterprises (China) (CN), Nanjing Medical University (CN), Nanjing University (CN), University of Hong Kong (HK), Stanford University (US)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 8%
Machine Learning in Healthcare
2.48
DOIOpen Access PDFOpenAlex
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