[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100646078":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":12,"locations":10,"responsibleParty":18,"collaborators":10,"id":22,"slug":23,"hasResults":24,"nctId":25,"briefTitle":26,"officialTitle":27,"acronym":28,"eligibilityCriteria":29,"healthyVolunteers":24,"sex":30,"minAge":31,"maxAge":10,"enrollmentInfo":32,"targetDuration":10,"studyType":35,"phases":10,"briefSummary":36,"conditions":37,"keywords":41,"overallStatus":50,"whyStopped":10,"lastUpdateSubmitDate":51,"lastUpdatePostDateStruct":52,"startDateStruct":55,"completionDateStruct":57,"leadSponsor":59,"locationsCount":10},{"fullName":5,"class":6},"Marmara University Pendik Training and Research Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":10},"elective surgery patients",null,"Adult patients (≥18 years) who underwent preoperative anesthesia evaluation before elective surgery. Anonymized structured vignettes derived from their records will be classified by four LLMs (ChatGPT, DeepSeek, Gemini, Claude) and by a blinded senior anesthesiologist panel serving as the reference standard.",[13],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},"Dilara Göçmen, Assistant Prof","CONTACT","+905413439438","dilara.gocmen@marmara.edu.tr",{"type":19,"investigatorFullName":20,"investigatorTitle":21,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","dilara gocmen","asistan prof","100646078","large-language-models-versus-anesthesiologists-for-asa-physical-status-classification-100646078",false,"NCT07696221","Large Language Models Versus Anesthesiologists for ASA Physical Status Classification","Comparison of Clinical Assessment and Large Language Models in Preoperative Risk Classification: A Retrospective Analysis of ChatGPT, DeepSeek, Gemini, and Claude in ASA Physical Status Classification","ASA-LLM","Inclusion Criteria:\n\n* Age 18 years or older\n* Planned elective surgery\n* Completed preoperative anesthesia evaluation\n\nExclusion Criteria:\n\n* Emergency surgical procedures\n* ASA VI (brain death)\n* Incomplete clinical records","ALL","18 Years",{"count":33,"type":34},350,"ESTIMATED","OBSERVATIONAL","The American Society of Anesthesiologists Physical Status (ASA-PS) classification is a cornerstone of preoperative risk assessment, yet interrater variability among clinicians is well documented. Large language models (LLMs) have recently demonstrated expert-level performance in several clinical classification tasks, including ASA-PS assignment.\n\nThis retrospective observational study evaluates whether four widely used LLMs - ChatGPT, DeepSeek, Gemini, and Claude - can accurately and consistently assign ASA-PS classes from structured, fully anonymized clinical vignettes derived from real preoperative anesthesia evaluations, using a consensus of senior anesthesiologists as the reference standard.\n\nNo patient data will be transmitted to third-party platforms. Clinical information will be converted by the investigators into de-identified structured vignettes containing only age range, sex, body mass index range, presence or absence of systemic diseases, functional capacity, and the major\u002Fminor nature of the planned surgery, in full compliance with national data protection legislation (KVKK).",[38,39,40],"Anesthesia","Preoperative Risk Prediction","Preoperative Risk Assessment",[42,43,44,45,46,47,48,49],"asa physical status","large language models","artificial intelligence","chatgpt","gemini","deepseek","Claude","risk classification","NOT_YET_RECRUITING","2026-07-06",{"date":53,"type":54},"2026-07-10","ACTUAL",{"date":56,"type":34},"2026-07-21",{"date":58,"type":34},"2026-10-21",{"name":5,"class":6}]