{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:06:56Z","timestamp":1789736816953,"version":"4.0.1"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Large language models (LLMs) offer significant potential for automating clinical trial classification by eligibility criteria. However, the optimal input data remain unclear: while abstracts provide a condensed signal, full-text articles contain substantially more information. Whether this additional signal improves performance or whether accompanying noise negatively affects the model\u2019s reasoning capabilities remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>GPT-5 was applied to classify 200 randomized controlled oncology trials, labelling whether patients with localized and\/or metastatic disease were eligible. Each trial was classified twice\u2014using the abstract and full text\u2014and outputs were compared with manually annotated ground-truth labels. Performance was assessed using accuracy, precision, recall, and F1 score, and statistical significance using the McNemar test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For identifying trials including patients with localized disease, GPT-5 achieved an accuracy of 86% (95% CI, 81%-91%; F1\u2009=\u20090.90) using abstracts and 92% (95% CI, 88%-95%; F1\u2009=\u20090.94) using full texts (P\u2009=\u2009.027). Performance for detecting trials, which include patients with metastatic disease, was comparably high (99% vs 98% accuracy; F1\u2009=\u20091.00-0.99). Overall accuracy for assigning combined labels increased from 86% (95% CI, 81%-91%) using abstracts to 92% (95% CI, 88%-95%) using full texts (P\u2009=\u2009.027).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusion<\/jats:title>\n                    <jats:p>Providing full-text articles to GPT-5 significantly improved the classification of oncology trials by eligibility criteria in this dataset. Full-text analysis appears particularly valuable for extracting eligibility criteria in oncology that are frequently omitted or not explicitly described within the abstract.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag179","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T11:42:17Z","timestamp":1787658137000},"source":"Crossref","is-referenced-by-count":0,"title":["More signal versus more noise: comparing full text and abstract as inputs for large language model-based classification of oncology trial eligibility criteria"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7975-1858","authenticated-orcid":false,"given":"Julia","family":"Weyrich","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University of Bern , Bern,","place":["Switzerland"]},{"name":"Department of Radiation Oncology, Cantonal Hospital Winterthur , Winterthur,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Dennst\u00e4dt","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern , Bern,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"F\u00f6rster","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cantonal Hospital Winterthur , Winterthur,","place":["Switzerland"]},{"name":"Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern , Bern,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Schr\u00f6der","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cantonal Hospital Winterthur , Winterthur,","place":["Switzerland"]},{"name":"Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern , Bern,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel M","family":"Aebersold","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern , Bern,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel R","family":"Zwahlen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cantonal Hospital Winterthur , Winterthur,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1040-4888","authenticated-orcid":false,"given":"Paul","family":"Windisch","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cantonal Hospital Winterthur , Winterthur,","place":["Switzerland"]},{"name":"Department of Radiation Oncology, Inselspital, Bern University Hospital, University of 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