[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"prediction-models\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:prediction-models":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,4,0,[8,63,89,121],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":44,"overallStatus":50,"whyStopped":4,"lastUpdateSubmitDate":51,"lastUpdatePostDateStruct":52,"startDateStruct":55,"completionDateStruct":57,"leadSponsor":59,"locationsCount":62},"100633082","early-functional-response-as-a-predictor-of-clinical-outcomes-in-hand-rehabilitation-a-prospective-feasibility-study-using-a-digital-monitoring-system-100633082",false,"NCT07522060","Early Functional Response as a Predictor of Clinical Outcomes in Hand Rehabilitation: A Prospective Feasibility Study Using a Digital Monitoring System","Early Functional Response as a Predictor of Clinical Outcomes in Hand Rehabilitation: A Prospective Feasibility Study Using a Clinician-Developed Digital Monitoring System","HANDREHPREDICT","Inclusion Criteria:\n\n* Adults aged 18 years or older\n* Hand or upper extremity pathology requiring rehabilitation, including but not limited to: flexor or extensor tendon injuries, peripheral nerve injuries or compression syndromes (including carpal tunnel syndrome, cubital tunnel syndrome), distal radius fractures, metacarpal or phalangeal fractures, joint injuries and instabilities, arthropathy (osteoarthritis, rheumatoid arthritis), trapeziometacarpal osteoarthritis, trigger finger, Dupuytren's disease, DeQuervain tenosynovitis, lateral epicondylitis and post-surgical upper extremity conditions\n* Under clinical responsibility of the principal investigator at the Faculty of Physical Therapy and Rehabilitation, Hacettepe University\n* Expected to attend minimum 8 rehabilitation sessions\n* Able to provide written informed consent\n\nExclusion Criteria:\n\n* • Active psychiatric illness precluding participation or completion of assessment tools\n\n  * Severe cognitive impairment precluding understanding of patient-reported outcome measures\n  * Literacy difficulties preventing self-completion of questionnaires\n  * Concurrent enrollment in another interventional clinical study\n  * Refusal to provide written informed consent","ALL","18 Years",{"count":20,"type":21},60,"ESTIMATED","OBSERVATIONAL","This study investigates whether early changes observed during the first weeks of hand and upper extremity rehabilitation can predict patient outcomes months later. In rehabilitation practice, clinicians make numerous decisions each session regarding exercise type, frequency, duration, and treatment approach. Most of these decisions are currently made without systematic longitudinal data. This study addresses three fundamental questions using data collected during routine clinical care: (1) Can the rate of improvement in the first weeks of treatment predict functional status months later, across both session-based and milestone-based time points? (2) Are there meaningfully different recovery profiles among hand rehabilitation patients? (3) Is there measurable variation in clinical decision-making among patients with similar profiles, and does this variation relate to outcomes? A digital patient monitoring platform developed by the principal investigator, a physiotherapist and academic researcher specializing in hand rehabilitation serves as the data collection infrastructure. The platform records standard clinical assessment measures in a structured format and has been in active clinical use at Hacettepe University prior to this study. For research purposes, the system has been expanded to include structured capture of patient-reported outcomes, patient global impression of change, treatment protocol coding, home exercise adherence, and automated calculation of early response metrics.\n\nThis is a 12-month prospective observational cohort study enrolling a minimum of 60 patients. Data are stored securely on the university's institutional network. Patients are anonymized using identification codes. The study is subject to Hacettepe University Ethics Committee approval and participant informed consent. Findings are expected to generate evidence supporting data-driven clinical decision-making in rehabilitation and to provide a feasibility foundation for a larger multi-center study.",[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"Hand","Hand Therapy","Upper Extremity","Prediction Models","Hand Injuries","Hand Injuries and Disorders","Hand Arthritis","Tendon Injuries","Wrist Fractures","Carpal Tunnel Syndrome (CTS)","Peripheral Nerve Injuries","Upper Extremity Injuries","Wrist Injuries","Finger Injuries","Trigger Finger","Trapeziometacarpal (TMC) Arthrosis","DeQuervain's Tenosynovitis","Cubital Tunnel Syndrome","Postoperative Rehabilitation",[45,46,47,48,49],"digital health monitoring","real-world evidence","clinical decision support","upper extremity rehabilitation","early response prediction","NOT_YET_RECRUITING","2026-04-03",{"date":53,"type":54},"2026-04-13","ACTUAL",{"date":56,"type":21},"2026-06-15",{"date":58,"type":21},"2027-06-01",{"name":60,"class":61},"Hacettepe University","OTHER",1,{"id":64,"slug":65,"hasResults":11,"nctId":66,"briefTitle":67,"officialTitle":68,"acronym":4,"eligibilityCriteria":69,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":70,"targetDuration":72,"studyType":22,"phases":4,"briefSummary":73,"conditions":74,"keywords":4,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":80,"lastUpdatePostDateStruct":81,"startDateStruct":83,"completionDateStruct":85,"leadSponsor":87,"locationsCount":62},"100633009","predictive-value-of-gastrointestinal-blood-flow-for-enteral-nutrition-intolerance-in-critically-ill-patients-100633009","NCT07521111","Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients","Study on the Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients","Inclusion Criteria:\n\n* Age \\> 18 years old.\n* Expected duration of enteral nutrition support \\> 7 days.\n* Patients or their legal representatives sign the informed consent form.\n\nExclusion Criteria:\n\n* History of major gastrointestinal surgery such as subtotal gastrectomy and gastrointestinal anastomosis.\n* Contraindications to abdominal point-of-care ultrasound (POCUS) examination (e.g., recent large-area abdominal burns, dressings blocking movement, open abdomen).\n* Presence of severe gastrointestinal diseases such as gastroparesis, intestinal obstruction, digestive tract perforation, and gastrointestinal bleeding upon admission.\n* Presence of severe peripheral vascular disease or valvular heart disease.\n* Pregnant or lactating women.",{"count":71,"type":21},500,"28 Days","This study aims to explore the correlation between gastrointestinal blood flow and the incidence of enteral nutrition intolerance (ENI) and its symptoms in critically ill patients, construct and compare predictive models including blood flow parameters, and evaluate their incremental predictive value.",[75,76,77,28,78],"Critical Illness","Enteral Nutrition Intolerance","Enteral Nutrition Feeding","Machine Learning","RECRUITING","2026-04-02",{"date":82,"type":54},"2026-04-09",{"date":84,"type":54},"2026-01-25",{"date":86,"type":21},"2027-06-30",{"name":88,"class":61},"Ruijin Hospital",{"id":90,"slug":91,"hasResults":11,"nctId":92,"briefTitle":93,"officialTitle":94,"acronym":4,"eligibilityCriteria":95,"healthyVolunteers":11,"sex":17,"minAge":96,"maxAge":18,"enrollmentInfo":97,"targetDuration":4,"studyType":99,"phases":100,"briefSummary":102,"conditions":103,"keywords":106,"overallStatus":79,"whyStopped":4,"lastUpdateSubmitDate":112,"lastUpdatePostDateStruct":113,"startDateStruct":115,"completionDateStruct":117,"leadSponsor":119,"locationsCount":62},"100585463","timely-ordering-of-pharmacogenetic-testing-100585463","NCT06902688","Timely Ordering of Pharmacogenetic Testing","Timely Ordering of Pharmacogenetic Testing in Pediatric Oncology","Inclusion Criteria:\n\n* Inpatient at The Hospital for Sick Children\n* Between 6 months to 18 years old\n\nExclusion Criteria:\n\n* Prior pharmacogenetic testing and\u002For prior receipt of a targeted medication\n* Current Intensive Care Unit (ICU) admission\n* Expected hospital discharge is prior to midnight on the day of admission","6 Months",{"count":98,"type":21},275,"INTERVENTIONAL",[101],"NA","The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question\\[s\\] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months:\n\n* Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication\n* Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication\n* Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results\n\nThis trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.",[78,28,104,105],"Pediatrics","Precision Medicine",[107,108,109,110,111],"precision medicine","machine learning","pharmacogenetics","prediction models","pediatrics","2026-03-03",{"date":114,"type":54},"2026-03-05",{"date":116,"type":54},"2025-06-10",{"date":118,"type":21},"2027-06-10",{"name":120,"class":61},"The Hospital for Sick Children",{"id":122,"slug":123,"hasResults":11,"nctId":124,"briefTitle":125,"officialTitle":126,"acronym":4,"eligibilityCriteria":127,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":128,"targetDuration":130,"studyType":22,"phases":4,"briefSummary":131,"conditions":132,"keywords":4,"overallStatus":50,"whyStopped":4,"lastUpdateSubmitDate":134,"lastUpdatePostDateStruct":135,"startDateStruct":137,"completionDateStruct":139,"leadSponsor":141,"locationsCount":4},"100618879","research-on-the-development-and-validation-of-an-early-prediction-model-for-delirium-100618879","NCT07337356","Research on the Development and Validation of an Early Prediction Model for Delirium","Research on the Development and Validation of an Early Prediction Model for Delirium Based on Machine Vision Analysis","Inclusion Criteria:\n\n* Age ≥ 18 years, expected ICU stay ≥ 24 hours, and informed consent to participate in this study;\n\nExclusion Criteria:\n\n* Patients with severe facial trauma\u002Fdeformities that prevent complete expression acquisition, and patients with a history of emotional problems (such as anxiety, depression, etc.).",{"count":129,"type":21},795,"7 Days","Delirium has a high incidence rate and significantly affects patient prognosis. Diagnosis often relies on manual assessment, which is subject to strong subjectivity, high rates of missed diagnosis, and poor stability. This study employs non-contact identification technology based on machine vision analysis to quantitatively analyze characteristic biological feature data such as micro-expressions. It then investigates the correlation between these features and delirium subtypes. By integrating clinical phenotypic data and using machine learning algorithms, a multi-modal early prediction model for delirium is constructed to meet the clinical need for early warning of delirium subtypes and enhance the efficacy of delirium identification.",[133,28,78],"Delirium","2026-01-04",{"date":136,"type":54},"2026-01-13",{"date":138,"type":21},"2026-02-01",{"date":140,"type":21},"2027-02-01",{"name":88,"class":61}]