{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:35:35Z","timestamp":1780554935755,"version":"3.54.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UM1 TR004538"],"award-info":[{"award-number":["UM1 TR004538"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["K12 TR004529"],"award-info":[{"award-number":["K12 TR004529"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01 AG082698"],"award-info":[{"award-number":["R01 AG082698"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the performance of a locally deployed adaptation of TrialGPT, a large language model (LLM) system for identifying trial-eligible patients from unstructured electronic health record (EHR) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>TrialGPT was re-engineered for secure, deployment at UT Health San Antonio using a locally hosted LLM. It was optimized for real-world data needs through a longitudinal patient\u2013encounter\u2013note hierarchy mirroring EHR documentation. Performance was evaluated in two stages: (1) benchmarking against an expert-adjudicated gold corpus (n\u2009=\u2009149) and (2) comparative validation against manual screening (n\u2009=\u200955).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Against the expert-adjudicated corpus, the system achieved 81.8% sensitivity, 97.8% specificity, and a positive predictive value of 75.0%. Compared with manual screening, it identified more than twice as many truly eligible patients (81.8% vs 36.4%) while preserving equivalent specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The adapted TrialGPT framework operationalizes trial matching, translating EHR data into actionable screening intelligence for efficient, scalable clinical trial recruitment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag006","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:44:59Z","timestamp":1768394699000},"page":"909-913","source":"Crossref","is-referenced-by-count":1,"title":["Translating evidence into practice: adapting TrialGPT for real-world clinical trial eligibility screening"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8978-1565","authenticated-orcid":false,"given":"Mahanazuddin","family":"Syed","sequence":"first","affiliation":[{"name":"University of Texas Health Science Center at San Antonio Department of Population Health Sciences, , San Antonio, TX 78229,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muayad","family":"Hamidi","sequence":"additional","affiliation":[{"name":"University of Texas Health Science Center at San Antonio Department of Population Health Sciences, , San Antonio, TX 78229,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manju","family":"Bikkanuri","sequence":"additional","affiliation":[{"name":"University of Texas Health Science Center at San Antonio Department of Population Health Sciences, , San Antonio, TX 78229,","place":["United 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