[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100624848":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":26,"centralContacts":31,"locations":26,"responsibleParty":37,"collaborators":26,"id":39,"slug":40,"hasResults":41,"nctId":42,"briefTitle":43,"officialTitle":44,"acronym":45,"eligibilityCriteria":46,"healthyVolunteers":41,"sex":47,"minAge":48,"maxAge":26,"enrollmentInfo":49,"targetDuration":26,"studyType":52,"phases":53,"briefSummary":55,"conditions":56,"keywords":58,"overallStatus":60,"whyStopped":26,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":26},{"fullName":5,"class":6},"China National Center for Cardiovascular Diseases","OTHER_GOV",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Control (Standard Manual Review)","ACTIVE_COMPARATOR","Physicians manually review all cases in the control set (n=54) with access to AI predictions and reasoning. No selective deferral.",[13],"Diagnostic Test: Standard Manual Review Workflow",{"label":15,"type":16,"description":17,"interventionNames":18},"Experimental (SCOUT-Assisted Review)","EXPERIMENTAL","Physicians process the intervention set (n=56) through the SCOUT framework. Low-uncertainty cases are auto-accepted; high-uncertainty cases undergo physician review with full audit trail.",[19],"Diagnostic Test: SCOUT-Assisted Review Workflow",[21,27],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":26},"DIAGNOSTIC_TEST","SCOUT-Assisted Review Workflow","SCOUT-Assisted Review (Intervention Arm): Physicians review 56 cases processed through the SCOUT framework. For cases classified as low-uncertainty (D(x)=0), the AI prediction is auto-accepted without physician review. For high-uncertainty cases (D(x)=1), the physician reviews the case with access to the main model's chain-of-thought reasoning and the meta-verification audit results. The main model is DeepSeek-V3.1 with chain-of-thought prompting.",[15],null,{"type":22,"name":28,"description":29,"armGroupLabels":30,"otherNames":26},"Standard Manual Review Workflow","Physicians perform a full manual review of 54 cases using raw medical records with access to the AI model's predictions and reasoning, but without SCOUT uncertainty stratification or selective deferral.",[9],[32],{"name":33,"role":34,"phone":35,"phoneExt":26,"email":36},"Xiaojin Gao, Dr.","CONTACT","+86 010 88322415","sophie_gao@sina.com",{"type":38,"investigatorFullName":26,"investigatorTitle":26,"investigatorAffiliation":26,"oldNameTitle":26,"oldOrganization":26},"SPONSOR","100624848","scalable-clinical-oversight-of-large-language-models-via-uncertainty-triangulation-100624848",false,"NCT07414966","Scalable Clinical Oversight of Large Language Models Via Uncertainty Triangulation","Prospective Evaluation of a Model-Agnostic Meta-Verification Framework (SCOUT) for Scalable Clinical Oversight of Large Language Model Outputs in Coronary Heart Disease Diagnosis: A Multi-Reader, Randomized, Crossover Trial","SCOUT","Inclusion Criteria:\n\n* Board-certified or in-training cardiologists at Fuwai Hospital\n* Spanning three experience strata: junior residents, senior residents, attending physicians\n\nExclusion Criteria:\n\n* Clinicians involved in the development or optimization of the SCOUT framework\n* Clinicians involved in the gold-standard adjudication process","ALL","18 Years",{"count":50,"type":51},7,"ESTIMATED","INTERVENTIONAL",[54],"NA","This prospective, multi-reader, randomized crossover trial evaluates SCOUT (Scalable Clinical Oversight via Uncertainty Triangulation), a model-agnostic meta-verification framework that selectively defers unreliable large language model (LLM) predictions to clinicians by triangulating three orthogonal uncertainty signals: model heterogeneity, stochastic inconsistency, and reasoning critique. The trial assesses whether SCOUT-assisted review can reduce physician review time compared with standard manual review of AI-generated diagnoses while maintaining non-inferior diagnostic accuracy in coronary heart disease (CHD) subtyping.",[57],"Coronary Heart Disease (CHD)",[59],"artificial intelligence","NOT_YET_RECRUITING","2026-02-14",{"date":63,"type":64},"2026-02-17","ACTUAL",{"date":66,"type":51},"2026-02-19",{"date":68,"type":51},"2026-02-28",{"name":5,"class":6}]