Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
This study investigates using Google's Gemini and Gemma LLMs to automate detection of EQ-5D studies in PubMed from abstracts. A multi-phase framework with few-shot prompting, weight ensembling, and soft stacking meta-classifier is proposed. Evaluating nine LLMs on expert-labeled data, the weighted ensemble of gemini-2.5-pro, gemma-3-12b, and gemma-3-27b achieved 0.74 weighted F1-score and accuracy, outperforming individual models. Ensembling improved precision-recall balance, and soft stacking enhanced reliability and interpretability. Results suggest ensemble LLMs are a reliable, scalable approach for automating biomedical screening.
[2606.19345] Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
[Submitted on 24 Apr 2026]
Title:Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
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Abstract:The rapid increase in scientific publications leads to the fact that manual study screening in systematic literature reviews (SLRs) is increasingly resource consuming, inefficient, and inconsistent. Classifying studies that clearly report health-related quality-of-life results, such as EQ-5D data, requires a high level of clinical interpretation and poses challenges for human reviewers. This study investigates the use of Google's Gemini and Gemma large language models (LLMs) in automating EQ-5D detection in the PubMed biomedical database based only on published abstracts. A multi-phase framework is proposed that integrates few-shot prompting, weight ensembling aggregation, and a soft stacking meta-classifier. Nine LLMs are evaluated on a dataset of PubMed studies manually labeled by two experts regarding EQ-5D reporting. The weighted ensemble of gemini-2.5-pro, gemma-3-12b, and gemma-3-27b obtained a 0.74 weighted F1-score and 0.74 accuracy, exceeding individually attained results. The ensembling of top-performing models improved the balance between precision and recall compared to individual models, while the soft stacking approach provided greater reliability and interpretability. Feature analysis shows that the probability results from the models are important in guiding the final predictions. The findings suggest that an ensemble-based LLM setup is a reliable and scalable approach for automating screening in biomedical research.
Comments: 6 pages, 7 tables, 8 equations
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.19345 [cs.CL]
(or arXiv:2606.19345v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.19345
arXiv-issued DOI via DataCite
Submission history
From: Zhyar Kwekha Rostam Mr. [view email] [v1] Fri, 24 Apr 2026 21:42:39 UTC (648 KB)
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