Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
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 stac...
| Authors | Zhyar Rzgar K. Rostam, Márta Péntek, János Tibor Czere et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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