Ignite Creation Date:
2025-12-24 @ 3:40 PM
Ignite Modification Date:
2025-12-28 @ 11:51 AM
Study NCT ID:
NCT06779292
Status:
COMPLETED
Last Update Posted:
2025-04-15
First Post:
2025-01-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Application of Large Language Models in Emergency Neurology
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D004630', 'term': 'Emergencies'}], 'ancestors': [{'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 433}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2025-02-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2025-04-07', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-04-14', 'studyFirstSubmitDate': '2025-01-06', 'studyFirstSubmitQcDate': '2025-01-15', 'lastUpdatePostDateStruct': {'date': '2025-04-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-04-07', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'dignostic accuracy', 'timeFrame': '1 month', 'description': 'To evaluate the consistency between the diagnosis made by large language models for emergency patients and the confirmed diagnosis after inpatient or outpatient visits.'}], 'secondaryOutcomes': [{'measure': 'Feasibility of treatment plans', 'timeFrame': '1 month', 'description': 'Experts use the Emergency Treatment Recommendation Scoring Scale to evaluate the treatment suggestions from conventional methods and large language models. The maximum score is 5 and the minimum score is 1, with 5 representing strong agreement with the recommendation.'}, {'measure': 'dignostic specificity', 'timeFrame': '1 month', 'description': 'A comparison of dianostic specificity between large language model diagnosis and emergency department physicians diagnosis'}, {'measure': 'Diagnostic Sensitivity', 'timeFrame': '1 month', 'description': 'A comparison of dianostic sensitivity between large language model diagnosis and emergency department physicians diagnosis.'}, {'measure': 'False Discovery Rate', 'timeFrame': '1 month', 'description': 'A comparison of the false discovery rate between large language model diagnosis and emergency department physicians diagnosis.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Neurology', 'Emergency']}, 'descriptionModule': {'briefSummary': 'Emergency neurology covers a wide range of conditions, often involving urgent situations such as acute cerebrovascular diseases, seizures, central nervous system infections, and consciousness disorders. However, due to the time constraints in emergency care and limited patient information collection, misdiagnosis and missed diagnoses are common issues. Large language models (LLMs) possess powerful natural language processing and knowledge reasoning capabilities, enabling them to directly handle and understand complex, unstructured medical data such as patient medical records, dialogue notes, and laboratory test results. LLMs show broad potential for application in complex medical scenarios. This study aims to evaluate the application value of LLMs in emergency neurology, specifically examining their diagnostic accuracy in emergency neurology conditions, analyzing the feasibility of treatment plans and further examination recommendations proposed by the model, and exploring their potential in improving diagnostic efficiency and aiding decision-making.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients in the emergency neurology department', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age ≥18-80 years, male or female.\n* Patients seeking emergency neurology care.\n* Patients who can provide complete medical records (including consultation recordings, physical examination, test results, etc.).\n* Voluntary participation and signing of informed consent.\n\nExclusion Criteria:\n\n* Patients who directly enter the resuscitation process due to the severity of their condition(e.g., patients who are immediately placed in the ICU).\n* Patients with unstable vital signs.\n* Patients who are unable to communicate effectively (e.g., severe consciousness impairment or severe cognitive disorders).\n* Patients who are currently participating in other clinical trials.'}, 'identificationModule': {'nctId': 'NCT06779292', 'briefTitle': 'Application of Large Language Models in Emergency Neurology', 'organization': {'class': 'OTHER', 'fullName': 'Capital Medical University'}, 'officialTitle': 'Application of Multimodal Large Language Models in Emergency Neurology Diagnosis', 'orgStudyIdInfo': {'id': 'ALEGN'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Patients presenting to the emergency neurology department.', 'interventionNames': ['Diagnostic Test: Large Language Model Diagnosis']}], 'interventions': [{'name': 'Large Language Model Diagnosis', 'type': 'DIAGNOSTIC_TEST', 'description': 'Using the large language model for diagnosing emergency neurology conditions.', 'armGroupLabels': ['Patients presenting to the emergency neurology department.']}]}, 'contactsLocationsModule': {'locations': [{'zip': '100053', 'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Xuanwu Hospital, Capital Medical University', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Capital Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor, Beijing Institute of Brain Disorders, Capital Medical University', 'investigatorFullName': 'Ji Xunming,MD,PhD', 'investigatorAffiliation': 'Capital Medical University'}}}}