[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100655154":3},{"organization":4,"outcomesModule":7,"designInfo":29,"detailedDescription":30,"studyPopulation":31,"armGroups":11,"interventions":32,"overallOfficials":11,"centralContacts":36,"locations":42,"responsibleParty":64,"collaborators":11,"id":68,"slug":69,"hasResults":70,"nctId":71,"briefTitle":72,"officialTitle":73,"acronym":11,"eligibilityCriteria":74,"healthyVolunteers":70,"sex":75,"minAge":11,"maxAge":11,"enrollmentInfo":76,"targetDuration":11,"studyType":79,"phases":11,"briefSummary":80,"conditions":81,"keywords":11,"overallStatus":44,"whyStopped":11,"lastUpdateSubmitDate":84,"lastUpdatePostDateStruct":85,"startDateStruct":88,"completionDateStruct":90,"leadSponsor":92,"locationsCount":93},{"fullName":5,"class":6},"Peking University Third Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":20,"otherOutcomes":11},[9,13,16,18],{"measure":10,"description":11,"timeFrame":12},"Coverage rate of known ADRs",null,"Up to 8 weeks",{"measure":14,"description":11,"timeFrame":15},"Objective question accuracy","Up to 24 weeks",{"measure":17,"description":11,"timeFrame":12},"Concordance rate of predicted unknown ADRs",{"measure":19,"description":11,"timeFrame":15},"Expert-rated subjective answer quality",[21,23,25,27],{"measure":22,"description":11,"timeFrame":15},"Subgroup differences in ADR recognition coverage rate",{"measure":24,"description":11,"timeFrame":15},"Inter-rater consistency",{"measure":26,"description":11,"timeFrame":15},"Subgroup differences in answer quality score",{"measure":28,"description":11,"timeFrame":15},"Rater acceptance scale score",{"allocation":11,"interventionModel":11,"interventionModelDescription":11,"primaryPurpose":11,"observationalModel":6,"timePerspective":6,"maskingInfo":11},"This observational study first retrospectively collects desensitization cases related to adverse drug reactions to construct a predictive model, and then prospectively enrolls patients to evaluate model efficacy and conduct comparative research with expert blind assessment.","This study includes two types of research subjects:\n\nRetrospective de-identified adverse drug reaction (ADR) medical records: A total of 253 ADR consultation cases covering anti-infectives, cardiovascular agents, anti-tumor drugs, central nervous system drugs and digestive system drugs. Each case contains complete medical history, medication records, ADR occurrence process and clinical outcome data, as well as at least one clinically confirmed definite ADR event.\n\nClinical evaluators: 20 licensed physicians or pharmacists holding intermediate or higher professional titles, with clinical pharmacy practice and regular participation in hospital ADR monitoring and consultation work.",[33],{"type":6,"name":34,"description":35,"armGroupLabels":11,"otherNames":11},"Retrospective medical record data analysis only, no clinical intervention","This study only analyzes de-identified historical electronic medical record data to build a multi-agent AI prediction model for adverse drug reactions. No drugs, medical devices, or clinical treatment interventions will be applied to any human subjects.",[37],{"name":38,"role":39,"phone":40,"phoneExt":11,"email":41},"Weilong Zhang","CONTACT","010-82266782","zhangwl2012@126.com",[43,59],{"facility":5,"status":44,"city":45,"state":46,"zip":11,"country":47,"countryCode":48,"cosmosGeoPoint":49,"geoPoint":54,"contacts":55},"RECRUITING","Beijing","Beijing Municipality","China","CN",{"type":50,"coordinates":51},"Point",[52,53],116.39723,39.9075,{"lat":53,"lon":52},[56],{"name":57,"role":39,"phone":40,"phoneExt":11,"email":58},"Hongmei Jing, Dr.","hongmeijing@bjmu.edu.cn",{"facility":5,"status":60,"city":45,"state":11,"zip":11,"country":47,"countryCode":48,"cosmosGeoPoint":61,"geoPoint":63,"contacts":11},"NOT_YET_RECRUITING",{"type":50,"coordinates":62},[52,53],{"lat":53,"lon":52},{"type":65,"investigatorFullName":66,"investigatorTitle":67,"investigatorAffiliation":5,"oldNameTitle":11,"oldOrganization":11},"PRINCIPAL_INVESTIGATOR","Hongmei Jing","Chief Physician","100655154","multi-agent-collaborative-adr-prediction-with-human-machine-decision-comparison-100655154",false,"NCT07808866","Multi-Agent Collaborative ADR Prediction With Human-Machine Decision Comparison","Multi-Agent Collaborative Framework for Adverse Drug Reaction Prediction: Evidence-Based Verification and Human-Machine Decision Comparative Study","Inclusion Criteria:\n\n* Cases shall involve drug categories including anti-infectives, cardiovascular agents, anti-tumor drugs, central nervous system drugs, digestive system drugs, etc. Each case must contain at least one definite adverse drug reaction (ADR) event, with complete supporting documentation (medical history, medication history, ADR occurrence process, and clinical outcome).\n\nExclusion Criteria:\n\n* Cases with incomplete supporting documentation lacking medical history, medication history, ADR occurrence process or clinical outcome.\n* Cases only with suspected or possible ADRs without definite clinical confirmation.","ALL",{"count":77,"type":78},20000,"ESTIMATED","OBSERVATIONAL","This study develops a multi-agent collaborative prediction model to forecast adverse drug reactions using real-world clinical medical records. It validates model performance via evidence-based data and compares decision outputs between the AI model and clinical physicians, aiming to improve early identification of drug adverse events. Only de-identified historical medical data will be analyzed; no new clinical interventions will be conducted, with no additional risks to participants.",[82,83],"Adverse Drug Reactions","Clinical Decision Support","2026-09-02",{"date":86,"type":87},"2026-09-09","ACTUAL",{"date":89,"type":87},"2026-06-19",{"date":91,"type":78},"2027-12-19",{"name":5,"class":6},2]