{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T17:16:33Z","timestamp":1788196593963,"version":"build-2803163510"},"reference-count":69,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Systematic literature reviews (SLRs) are essential for evidence synthesis in health research but remain labor-intensive, especially at the screening stage. Manual review of titles and abstracts requires substantial human effort, while existing automation tools still have limited adoption in health technology assessment. The EQ-5D questionnaire, a widely used patient-reported outcome measure for health-related quality of life, provides data that frequently underpin reimbursement and policy decisions.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This pilot study evaluated whether recent large language models (LLMs) can support the identification of publications reporting EQ-5D data in PubMed records, using only publicly available metadata (title, abstract, and keywords).<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A total of 200 publications retrieved through the EuroQol PubMed filter were manually labeled by experts as reporting or not reporting EQ-5D data. The dataset was split into stratified training, validation, and test subsets. Several machine learning approaches were compared, including a Na\u00efve Bayes baseline using bag-of-words features, a decision-tree model based on full-text keyword occurrence, and transformer-based LLMs (Bidirectional Encoder Representations from Transformers [BERT], Biomedical BERT [BioBERT], Scientific BERT [SciBERT], and Biomedical Language Understanding Evaluation BERT [BlueBERT]). Both classifier-only and fine-tuned configurations were tested across multiple learning rates. Model performance was assessed using accuracy, precision, recall, and\n F<\/jats:italic>\n 1<\/jats:sub>\n -score.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Baseline approaches achieved near-random test performance (accuracy around 0.53). Classifier-only LLMs modestly improved results (accuracy up to 0.64 with SciBERT). Fine-tuned models substantially outperformed these baselines, with BERT and BioBERT achieving the best performance (accuracy=0.70;\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=0.68). In screening-oriented evaluation, this configuration achieved 90.0% sensitivity, 40.0% specificity, and 6 false negatives on the held-out test set. The models reproduced human screening tendencies despite the small dataset size, demonstrating the technical feasibility of LLM-assisted article selection.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study provides the first demonstration of LLM-assisted identification of EQ-5D data in biomedical literature. The findings support technical feasibility but do not establish a reliable stand-alone automated screening tool. Although limited by dataset size, the proposed workflow is reproducible and adaptable to other patient-reported outcome measures. Because validation was based on a single small train-validation-test split, the results should be interpreted as preliminary; future work will scale data collection, include statistical testing, and explore semisupervised learning to further reduce manual screening workload.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/86647","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T17:11:24Z","timestamp":1788196284000},"page":"e86647-e86647","source":"Crossref","is-referenced-by-count":0,"title":["Toward Automating the Selection of Articles Reporting EQ-5D Data for Systematic Literature Reviews Using Large Language Models: Algorithm Development and Evaluation Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8845-8301","authenticated-orcid":false,"given":"G\u00e1bor","family":"Kert\u00e9sz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-7906","authenticated-orcid":false,"given":"J\u00e1nos Tibor","family":"Czere","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1992-6087","authenticated-orcid":false,"given":"Zsombor","family":"Zrubka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9285-8746","authenticated-orcid":false,"given":"Laszlo","family":"Gulacsi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9636-6012","authenticated-orcid":false,"given":"Marta","family":"Pentek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"R1","unstructured":"Cochrane handbook for systematic reviews of interventions (current version). 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