[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100655237":3},{"organization":4,"outcomesModule":7,"designInfo":13,"detailedDescription":11,"studyPopulation":15,"armGroups":11,"interventions":16,"overallOfficials":11,"centralContacts":11,"locations":11,"responsibleParty":20,"collaborators":11,"id":24,"slug":25,"hasResults":26,"nctId":27,"briefTitle":28,"officialTitle":29,"acronym":11,"eligibilityCriteria":30,"healthyVolunteers":26,"sex":31,"minAge":32,"maxAge":33,"enrollmentInfo":34,"targetDuration":11,"studyType":37,"phases":11,"briefSummary":38,"conditions":39,"keywords":11,"overallStatus":41,"whyStopped":11,"lastUpdateSubmitDate":42,"lastUpdatePostDateStruct":43,"startDateStruct":46,"completionDateStruct":48,"leadSponsor":50,"locationsCount":11},{"fullName":5,"class":6},"Tanta University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":11,"otherOutcomes":11},[9],{"measure":10,"description":11,"timeFrame":12},"Distinct patient subgroups (phenotypes) identified through unsupervised machine learning clustering algorithms.",null,"At the time of questionnaire completion (Baseline)",{"allocation":11,"interventionModel":11,"interventionModelDescription":11,"primaryPurpose":11,"observationalModel":6,"timePerspective":14,"maskingInfo":11},"CROSS_SECTIONAL","A convenience sample of adult patients with a physician-confirmed diagnosis of Irritable Bowel Syndrome (IBS) recruited from online health communities, clinical networks, and social media platforms. The population includes individuals across various demographic backgrounds, IBS subtypes, and symptom severity levels, who are capable of completing a digital questionnaire regarding their clinical and lifestyle characteristics.",[17],{"type":6,"name":18,"description":19,"armGroupLabels":11,"otherNames":11},"Questionnaire and Physical Exam","ONLINE QUESTIONNAIRE",{"type":21,"investigatorFullName":22,"investigatorTitle":23,"investigatorAffiliation":5,"oldNameTitle":11,"oldOrganization":11},"PRINCIPAL_INVESTIGATOR","Khadija Ahmed Mhrose Glal","Director","100655237","cl-pharm-ibs-ml-2026-100655237",false,"NCT07809646","Cl-Pharm-IBS-ML-2026","Machine Learning-Based Phenotyping of IBS Patients Using Multidimensional Patient-Reported Outcomes","Inclusion Criteria:\n\n* Participants aged 18 years or older.\n* Formal physician-confirmed diagnosis of Irritable Bowel Syndrome (IBS).\n* Ability to access and complete the digital multidimensional questionnaire.\n* Willingness to provide digital informed consent.\n\nExclusion Criteria:\n\n* Presence of organic gastrointestinal diseases (e.g., Inflammatory Bowel Disease, celiac disease, or colorectal cancer).\n* Presence of alarm\u002Fred flag symptoms (e.g., nocturnal symptoms waking the patient).\n* Incomplete or inconsistent questionnaire responses.","ALL","18 Years","65 Years",{"count":35,"type":36},500,"ESTIMATED","OBSERVATIONAL","The purpose of this study is to identify distinct subgroups (phenotypes) of Irritable Bowel Syndrome (IBS) patients. While traditional IBS classification relies mainly on bowel habits, this research uses a multidimensional questionnaire to capture clinical symptoms, psychological factors, diet triggers, sleep quality, and digital behaviors. By applying advanced machine learning algorithms to these patient-reported outcomes, the study aims to uncover hidden patterns that can help customize future treatments and improve patient care.",[40],"IBS","NOT_YET_RECRUITING","2026-09-09",{"date":44,"type":45},"2026-09-11","ACTUAL",{"date":47,"type":36},"2026-09-20",{"date":49,"type":36},"2027-01-01",{"name":5,"class":6}]