[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100585463":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":24,"locations":33,"responsibleParty":50,"collaborators":19,"id":53,"slug":54,"hasResults":55,"nctId":56,"briefTitle":57,"officialTitle":58,"acronym":19,"eligibilityCriteria":59,"healthyVolunteers":55,"sex":60,"minAge":61,"maxAge":62,"enrollmentInfo":63,"targetDuration":19,"studyType":66,"phases":67,"briefSummary":69,"conditions":70,"keywords":75,"overallStatus":35,"whyStopped":19,"lastUpdateSubmitDate":81,"lastUpdatePostDateStruct":82,"startDateStruct":85,"completionDateStruct":87,"leadSponsor":89,"locationsCount":90},{"fullName":5,"class":6},"The Hospital for Sick Children","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"ML model","EXPERIMENTAL","Participants predicted by an ML model to receive a \"targeted medication\" within three months following admission.",[13],"Other: ML-based intervention",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":19},"ML-based intervention","A ML-based model will predict and identify participants that are at high-risk of receiving a targeted medication within three months after their hospital admission date.",[9],null,[21],{"name":22,"affiliation":5,"role":23},"Lillian Sung, MD, PhD","PRINCIPAL_INVESTIGATOR",[25,29],{"name":22,"role":26,"phone":27,"phoneExt":19,"email":28},"CONTACT","4168135287","lillian.sung@sickkids.ca",{"name":30,"role":26,"phone":31,"phoneExt":32,"email":28},"Agata Wolochacz, BMSc","4168137654","309976",[34],{"facility":5,"status":35,"city":36,"state":37,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":41,"geoPoint":46,"contacts":47},"RECRUITING","Toronto","Ontario","M5G1X8","Canada","CA",{"type":42,"coordinates":43},"Point",[44,45],-79.39864,43.70643,{"lat":45,"lon":44},[48,49],{"name":22,"role":26,"phone":27,"phoneExt":19,"email":28},{"name":22,"role":23,"phone":19,"phoneExt":19,"email":19},{"type":23,"investigatorFullName":51,"investigatorTitle":52,"investigatorAffiliation":5,"oldNameTitle":19,"oldOrganization":19},"Lillian Sung","Chief Clinical Data Scientist, Paediatric Oncologist","100585463","timely-ordering-of-pharmacogenetic-testing-100585463",false,"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","ALL","6 Months","18 Years",{"count":64,"type":65},275,"ESTIMATED","INTERVENTIONAL",[68],"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.",[71,72,73,74],"Machine Learning","Prediction Models","Pediatrics","Precision Medicine",[76,77,78,79,80],"precision medicine","machine learning","pharmacogenetics","prediction models","pediatrics","2026-03-03",{"date":83,"type":84},"2026-03-05","ACTUAL",{"date":86,"type":84},"2025-06-10",{"date":88,"type":65},"2027-06-10",{"name":5,"class":6},1]