[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100618587":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":12,"centralContacts":19,"locations":24,"responsibleParty":53,"collaborators":55,"id":58,"slug":59,"hasResults":60,"nctId":61,"briefTitle":62,"officialTitle":63,"acronym":64,"eligibilityCriteria":65,"healthyVolunteers":60,"sex":66,"minAge":67,"maxAge":7,"enrollmentInfo":68,"targetDuration":7,"studyType":71,"phases":7,"briefSummary":72,"conditions":73,"keywords":7,"overallStatus":27,"whyStopped":7,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":86,"locationsCount":87},{"fullName":5,"class":6},"Istituto Ortopedico Rizzoli","OTHER",null,[9],{"type":6,"name":10,"description":11,"armGroupLabels":7,"otherNames":7},"Predictive Model for Early Mobility Recovery and Length of Stay","Application of a machine learning-based predictive algorithm to retrospectively and prospectively analyze clinical and perioperative data in patients undergoing hip or knee arthroplasty, without influencing clinical decision-making.",[13,17],{"name":14,"affiliation":15,"role":16},"Mattia Morri","IRCCS Istotuto Ortopedico Rizzoli","PRINCIPAL_INVESTIGATOR",{"name":18,"affiliation":15,"role":16},"Morri",[20],{"name":14,"role":21,"phone":22,"phoneExt":7,"email":23},"CONTACT","+390516366694","mattia.morri@ior.it",[25,40],{"facility":26,"status":27,"city":28,"state":7,"zip":29,"country":30,"countryCode":31,"cosmosGeoPoint":32,"geoPoint":37,"contacts":38},"SAITeR IRCCS Istituto Ortopedico Rizzoli","RECRUITING","Bologna","40100","Italy","IT",{"type":33,"coordinates":34},"Point",[35,36],11.33875,44.49381,{"lat":36,"lon":35},[39],{"name":14,"role":21,"phone":22,"phoneExt":7,"email":23},{"facility":41,"status":42,"city":43,"state":7,"zip":7,"country":30,"countryCode":31,"cosmosGeoPoint":44,"geoPoint":48,"contacts":49},"Azienda U.S.L. - IRCCS di Reggio Emilia","NOT_YET_RECRUITING","Reggio Emilia",{"type":33,"coordinates":45},[46,47],10.63125,44.69825,{"lat":47,"lon":46},[50],{"name":51,"role":21,"phone":7,"phoneExt":7,"email":52},"Alessia Pecorari","alessia.pecorari@ausl.re.it",{"type":54,"investigatorFullName":7,"investigatorTitle":7,"investigatorAffiliation":7,"oldNameTitle":7,"oldOrganization":7},"SPONSOR",[56],{"name":41,"class":57},"UNKNOWN","100618587","development-and-pre-validation-of-a-machine-learning-based-prediction-algorithm-for-early-functional-recovery-in-patients-undergoing-hip-and-knee-replacement-surgery-100618587",false,"NCT07333560","Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery","Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.","FISIO_IA","Inclusion Criteria:\n\n* Adults aged 18 years or older\n* Patients underwent elective hip or knee arthroplasty.\n* Patients for whom postoperative physiotherapy was initiated.\n\nExclusion Criteria:\n\n* Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.\n* Patients for whom postoperative physiotherapy was not provided due to postoperative complications\n* clinical data are unavailable.","ALL","18 Years",{"count":69,"type":70},943,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is:\n\nCan a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery?\n\nPatients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.",[74,75,76,77],"Artificial Intelligence (AI)","Machine Learning","Joint Replacement","Predictive Model","2026-05-27",{"date":80,"type":81},"2026-06-01","ACTUAL",{"date":83,"type":81},"2026-03-09",{"date":85,"type":70},"2027-12",{"name":5,"class":6},2]