An ensemble of three Google LLMs achieves a 0.74 weighted F1-score for detecting EQ-5D reporting in 200 PubMed abstracts, marginally outperforming individual models.
Towards automating the selection of articles reporting eq-5d data for system- atic literature reviews using large language models
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Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
An ensemble of three Google LLMs achieves a 0.74 weighted F1-score for detecting EQ-5D reporting in 200 PubMed abstracts, marginally outperforming individual models.