[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657856":3},{"organization":4,"outcomesModule":7,"designInfo":32,"detailedDescription":42,"studyPopulation":31,"armGroups":43,"interventions":56,"overallOfficials":31,"centralContacts":68,"locations":78,"responsibleParty":127,"collaborators":31,"id":131,"slug":132,"hasResults":133,"nctId":134,"briefTitle":135,"officialTitle":136,"acronym":137,"eligibilityCriteria":138,"healthyVolunteers":133,"sex":139,"minAge":140,"maxAge":31,"enrollmentInfo":141,"targetDuration":31,"studyType":144,"phases":145,"briefSummary":147,"conditions":148,"keywords":150,"overallStatus":81,"whyStopped":31,"lastUpdateSubmitDate":155,"lastUpdatePostDateStruct":156,"startDateStruct":159,"completionDateStruct":161,"leadSponsor":163,"locationsCount":164},{"fullName":5,"class":6},"Capital Medical University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":31},[9],{"measure":10,"description":11,"timeFrame":12},"Condition category concordance","The primary outcome of the LAMP-2 trial was condition category concordance, defined as at least one condition listed in a participant's response falling within the same prespecified neurological disease category as the final reference diagnosis.","30 days",[14,18,21,25,28],{"measure":15,"description":16,"timeFrame":17},"Preparation for Decision Making Scale","Preparation for Decision Making Scale (PrepDM) is a self-reported scale used to assess participants' readiness for medical decision-making after medical consultation. The scale includes 10 items. Each item is scored from 1 to 5 using a 5-point Likert scale. The total score is the mean of the 10 items. Higher scores indicate greater preparation for decision-making.","Within 1 hour",{"measure":19,"description":20,"timeFrame":17},"Consultation duration","Consultation duration is defined as the time from the patient's arrival at the outpatient clinic to the completion of the outpatient visit.",{"measure":22,"description":23,"timeFrame":24},"Patient satisfaction with this consultation","Patient satisfaction is a self-reported measure used to assess participants' satisfaction with the outpatient visit. Higher scores indicate greater satisfaction.","Within 1 hour after the outpatient visit",{"measure":26,"description":27,"timeFrame":24},"Red-flag identification","Red-flag identification is defined as the proportion of participants whose final response includes the key warning signs predefined by experts for the participant's presenting symptoms.",{"measure":29,"description":30,"timeFrame":17},"NASA-TLX score","NASA-TLX score is a self-reported task-load score measured immediately after the LLM-assisted medical consultation with ChatGPT. It includes six domains: mental demand, physical demand, temporal demand, effort, frustration, and performance. Each domain is scored from 0 to 100. The total score is the mean of the six domains. Higher scores indicate greater perceived task load.",null,{"allocation":33,"interventionModel":34,"interventionModelDescription":35,"primaryPurpose":36,"observationalModel":31,"timePerspective":31,"maskingInfo":37},"RANDOMIZED","PARALLEL","Participants will be randomly assigned in a 1:1 ratio to the 3M-6D education GPT group or the GPT group.","HEALTH_SERVICES_RESEARCH",{"masking":38,"maskingDescription":39,"whoMasked":40},"SINGLE","Outcome assessors will be blinded to group assignment when evaluating participants' outcomes. Group information will be removed from the outcomes before assessment.",[41],"OUTCOMES_ASSESSOR","This randomized, controlled clinical trial will evaluate whether three-minute six-dimension education (3M-6D education) can improve the reliability of large language models (LLMs) as medical assistants in neurology outpatients.\n\nEligible participants will be randomly assigned in a 1:1 ratio to the 3M-6D education GPT group or the GPT group. Participants in the 3M-6D education GPT group will first receive approximately three minutes of structured education and then use ChatGPT for medical consultation. Participants in the GPT group will directly use ChatGPT for medical consultation without receiving 3M-6D education. All participants will communicate with ChatGPT using unrestricted natural language.\n\nThe 3M-6D education is a structured framework designed to help patients describe their symptoms and provide more complete medical information during LLM-assisted medical consultation. It guides patients to describe six key dimensions.",[44,51],{"label":45,"type":46,"description":47,"interventionNames":48},"3M-6D education GPT Group","EXPERIMENTAL","Participants will first be trained in 3M-6D education, then use ChatGPT to complete a consultation task in unrestricted natural language.",[49,50],"Behavioral: three minutes six dimensions education","Other: ChatGPT",{"label":52,"type":53,"description":54,"interventionNames":55},"GPT Group","ACTIVE_COMPARATOR","Participants will use ChatGPT to complete a consultation task in unrestricted natural language.",[50],[57,64],{"type":58,"name":59,"description":60,"armGroupLabels":61,"otherNames":62},"BEHAVIORAL","three minutes six dimensions education","3M-6D education is designed based on Cognitive Load Theory to reduce the cognitive burden on patients during medical interactions with AI and to improve the clarity and completeness of symptom reporting. Guided by cognitive load theory and the natural process physicians use to take medical histories, the investigators identified candidate information dimensions and developed a structured expression framework with six dimensions for public health queries through a Delphi expert consensus process. Participants were instructed to use the framework to describe their symptoms across these six dimensions; this process can typically be completed within three minutes, so the investigators call this approach three minutes six dimensions education (3M-6D education).",[45],[63],"3M-6D education",{"type":6,"name":65,"description":66,"armGroupLabels":67,"otherNames":31},"ChatGPT","Participants use ChatGPT to complete a consultation task in unrestricted natural language.",[45,52],[69,74],{"name":70,"role":71,"phone":72,"phoneExt":31,"email":73},"Xunming Ji","CONTACT","01083198962","jixm@ccmu.edu.cn",{"name":75,"role":71,"phone":76,"phoneExt":31,"email":77},"Chuanjie Wu","01083199439","wuchuanjie@ccmu.edu.cn",[79,97,107,117],{"facility":80,"status":81,"city":82,"state":83,"zip":31,"country":84,"countryCode":85,"cosmosGeoPoint":86,"geoPoint":91,"contacts":92},"Aerospace Center Hospital","RECRUITING","Beijing","Beijing Municipality","China","CN",{"type":87,"coordinates":88},"Point",[89,90],116.39723,39.9075,{"lat":90,"lon":89},[93],{"name":94,"role":71,"phone":95,"phoneExt":31,"email":96},"Hui Lu","86+18844815888","gyibngc1@163.com",{"facility":98,"status":81,"city":82,"state":83,"zip":31,"country":84,"countryCode":85,"cosmosGeoPoint":99,"geoPoint":101,"contacts":102},"Huimin Hospital",{"type":87,"coordinates":100},[89,90],{"lat":90,"lon":89},[103],{"name":104,"role":71,"phone":105,"phoneExt":31,"email":106},"Peng Yu","86+13500129973","luojiaqi1008@126.com",{"facility":108,"status":81,"city":82,"state":83,"zip":31,"country":84,"countryCode":85,"cosmosGeoPoint":109,"geoPoint":111,"contacts":112},"Mentougou District Hospital",{"type":87,"coordinates":110},[89,90],{"lat":90,"lon":89},[113],{"name":114,"role":71,"phone":115,"phoneExt":31,"email":116},"Yanbo Zhao","86+15097941106","a15097941106@163.com",{"facility":118,"status":81,"city":82,"state":31,"zip":31,"country":84,"countryCode":85,"cosmosGeoPoint":119,"geoPoint":121,"contacts":122},"Xuanwu Hospita",{"type":87,"coordinates":120},[89,90],{"lat":90,"lon":89},[123,125],{"name":124,"role":71,"phone":76,"phoneExt":31,"email":77},"Chuanjie Wu, MD",{"name":126,"role":71,"phone":72,"phoneExt":31,"email":73},"Xunming Ji, PhD\u002FMD",{"type":128,"investigatorFullName":129,"investigatorTitle":130,"investigatorAffiliation":5,"oldNameTitle":31,"oldOrganization":31},"PRINCIPAL_INVESTIGATOR","Ji Xunming,MD,PhD","Principal Investigator","100657856","3m-6d-educational-intervention-to-improve-llm-assisted-medical-consultation-in-neurology-outpatients-100657856",false,"NCT07844044","3M-6D Educational Intervention to Improve LLM-Assisted Medical Consultation in Neurology Outpatients","Improving the Reliability of LLM-Assisted Medical Consultation in Neurology Outpatients Through a 3M-6D Structured Educational Intervention: a Randomized Controlled Trial","LAMP-2","Inclusion Criteria:\n\n1. Aged ≥ 18 years\n2. Completed primary school or higher education;\n3. Able to use a smartphone or computer to complete online interactions;\n4. Presenting to the hospital for neurological symptoms;\n5. This visit is the first consultation for the current symptoms;\n6. Has not used AI tools or online resources to assess the current condition prior to this visit;\n7. Able to understand and comply with study procedures and to provide written informed consent.\n\nExclusion Criteria:\n\n1. Currently or previously employed as a healthcare worker;\n2. Previously received systematic medical training;\n3. Condition requires emergency care;\n4. Participating in another clinical trial that may affect this study's outcomes;\n5. Any other condition the investigators consider unsuitable for enrollment or likely to interfere with trial results.","ALL","18 Years",{"count":142,"type":143},396,"ESTIMATED","INTERVENTIONAL",[146],"NA","This study will evaluate whether three-minute six-dimension education (3M-6D education) can improve the reliability of large language models (LLMs) as medical assistants in neurology outpatients. Participants will be randomly assigned to receive or not receive 3M-6D education before using ChatGPT for medical consultation.",[149],"Condition Category Concordance",[63,151,152,153,154],"Large Language Models","Cognitive Load Theory","neurology outpatients","condition category concordance","2026-09-29",{"date":157,"type":158},"2026-10-02","ACTUAL",{"date":160,"type":158},"2026-09-30",{"date":162,"type":143},"2026-10-31",{"name":5,"class":6},4]