[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100557171":3},{"organization":4,"armGroups":7,"interventions":7,"overallOfficials":7,"centralContacts":8,"locations":17,"responsibleParty":38,"collaborators":7,"id":41,"slug":42,"hasResults":43,"nctId":44,"briefTitle":45,"officialTitle":46,"acronym":47,"eligibilityCriteria":48,"healthyVolunteers":43,"sex":49,"minAge":7,"maxAge":7,"enrollmentInfo":50,"targetDuration":7,"studyType":53,"phases":7,"briefSummary":54,"conditions":55,"keywords":57,"overallStatus":20,"whyStopped":7,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":70},{"fullName":5,"class":6},"University of Zurich","OTHER",null,[9,14],{"name":10,"role":11,"phone":12,"phoneExt":7,"email":13},"Laura Philine Westphal, MD","CONTACT","0041432537590","lauraphiline.westphal@usz.ch",{"name":15,"role":11,"phone":7,"phoneExt":7,"email":16},"Susanne Wegener, MD","susanne.wegener@usz.ch",[18],{"facility":19,"status":20,"city":21,"state":7,"zip":22,"country":23,"countryCode":24,"cosmosGeoPoint":25,"geoPoint":30,"contacts":31},"University Hospital Zurich, Department of Neurology","RECRUITING","Zurich","8091","Switzerland","CH",{"type":26,"coordinates":27},"Point",[28,29],8.55,47.36667,{"lat":29,"lon":28},[32,34,36],{"name":33,"role":11,"phone":12,"phoneExt":7,"email":13},"Laura Westphal, MD",{"name":15,"role":11,"phone":35,"phoneExt":7,"email":16},"0041432539574",{"name":15,"role":37,"phone":7,"phoneExt":7,"email":7},"PRINCIPAL_INVESTIGATOR",{"type":37,"investigatorFullName":39,"investigatorTitle":40,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"Susanne Wegener","Prof. Dr. med.","100557171","stop-stroke-stroke-outcome-prediction-in-the-acute-treatment-setting-100557171",false,"NCT06534645","STOP-stroke: STroke Outcome Prediction in the Acute Treatment Setting","STOP-stroke: STroke Outcome Prediction in the Acute Treatment Setting - a Prospective, Single-center, Observational Study","STOP-STroke","Inclusion Criteria:\n\n* Patients from up to 18 years years of age without any upper age limit.\n* Patients with clinical suspicion of acute ischemic stroke (acute onset focal neurological deficit) at the discretion of the paramedic or treating physician within 24 hours of symptom onset including wake-up situation and unclear symptom onset planned for clinically indicated neuroimaging.\n* Patients with externally performed neuroimaging before admission or referral to the USZ will be included from the time point N2 on if no refusal of use of data is documented.\n\nExclusion Criteria:\n\n• Patients with documented objection of subsequent use of personal health data or patients who reject the use of personal health data during follow-up after initial informed consent by an independent physician in the acute setting. We will not include patients in the study if there is no written informed consent either from the patient her-\u002Fhimself, the next of kin or the independent physician.","ALL",{"count":51,"type":52},250,"ESTIMATED","OBSERVATIONAL","The STOP-stroke project aims at improving prediction of outcome early after stroke. In order to achieve this, we need to understand reasons (important variables) for prediction in a real clinical prognostication process.\n\nWe aim to:\n\n1. Test the predictive performance of stroke neurologists for outcome prediction (NIHSS at 24 hours and 3 months and mRS at 3 months after stroke onset) prospectively and in a real clinical setting, and to explore the most important baseline variables in their prognostication process.\n2. Test the prediction performance of our DL models when being provided with structured clinical and\u002For imaging information from the same patients as the neurologists; and to discover most relevant features of the input data.\n3. Use the information gained from our experiments for improving our DL algorithm. This will include an error analysis on the missclassifications of models and neurologists to understand the pitfalls of both approaches. We anticipate to develop a robust, reliable and clinically feasible application ready for testing in a prospective, observational trial.",[56],"Stroke Outcome Prediction Supported by Deep Learning Algorithm",[58,59,60],"stroke outcome prediction","deep learning","AI in stroke","2026-05-03",{"date":63,"type":64},"2026-05-07","ACTUAL",{"date":66,"type":64},"2024-10-29",{"date":68,"type":52},"2027-07-30",{"name":5,"class":6},1]