[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100618299":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":20,"locations":10,"responsibleParty":28,"collaborators":10,"id":32,"slug":33,"hasResults":34,"nctId":35,"briefTitle":36,"officialTitle":37,"acronym":10,"eligibilityCriteria":38,"healthyVolunteers":34,"sex":39,"minAge":40,"maxAge":41,"enrollmentInfo":42,"targetDuration":10,"studyType":45,"phases":10,"briefSummary":46,"conditions":47,"keywords":10,"overallStatus":49,"whyStopped":10,"lastUpdateSubmitDate":50,"lastUpdatePostDateStruct":51,"startDateStruct":54,"completionDateStruct":56,"leadSponsor":58,"locationsCount":10},{"fullName":5,"class":6},"Asian Institute of Gastroenterology, India","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Single prospective observational cohort",null,"Participants undergo standard-of-care colonoscopy\n\nNo allocation into treatment or comparison arms",[13],"Procedure: Not Applicable \u002F Observational study",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"PROCEDURE","Not Applicable \u002F Observational study","No study-specific intervention is administered. Participants undergo standard-of-care diagnostic colonoscopy and histopathological evaluation. A locked machine-learning model is applied to routinely collected baseline clinical and demographic data for risk prediction only, without influencing clinical management.",[9],[21,26],{"name":22,"role":23,"phone":24,"phoneExt":10,"email":25},"DR. NITIN JAGTAP, MD,DM","CONTACT","8712015028","docsnitin13@gmail.com",{"name":27,"role":23,"phone":24,"phoneExt":10,"email":25},"DR NITIN JAGTAP, MD,DM",{"type":29,"investigatorFullName":30,"investigatorTitle":31,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Mohan Ramchandani","Consultant Gastroenterology","100618299","external-multicentre-validation-of-a-machine-learning-model-to-predict-colonic-adenoma-in-indian-adults-100618299",false,"NCT07329816","External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults","External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study","Inclusion Criteria:\n\n* Adults ≥18 years undergoing diagnostic colonoscopy.\n* Adequate bowel preparation (Boston Bowel Preparation Scale total ≥6 with each segment ≥2).\n* Complete examination (cecal intubation; withdrawal time ≥6 min when no therapy).\n* Availability of all model predictors per CRF.\n\nExclusion Criteria:\n\n* • Known CRC or polyp, prior colectomy, polyposis syndromes, known IBD, or strong hereditary CRC syndromes (e.g., Lynch) if excluded in derivation.\n\n  * Inadequate prep, incomplete colonoscopy, obstructing lesions preventing optical diagnosis beyond obstruction.\n  * Emergency colonoscopies, therapeutic-only procedures without diagnostic intent.","ALL","18 Years","75 Years",{"count":43,"type":44},1000,"ESTIMATED","OBSERVATIONAL","Colorectal adenomas are precursors to colorectal cancer (CRC). Accurate pre-procedure risk stratification could optimize colonoscopy yield and resource allocation in India, where adenoma prevalence varies by age, sex, and lifestyle\u002Fmetabolic factors. ML models can integrate multiple predictors to estimate individualized risk.\n\nExisting risk scores are largely Western; performance and calibration may not be appropriate in Indian populations with different socio-demographic and metabolic profiles. External, prospective, multicentre validation is essential before clinical implementation.",[48],"Colonoscopy","NOT_YET_RECRUITING","2026-01-09",{"date":52,"type":53},"2026-01-12","ACTUAL",{"date":55,"type":44},"2026-02-01",{"date":57,"type":44},"2027-03-30",{"name":5,"class":6}]