[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Grand Hôpital de Charleroi\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":107},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,4,0,[8,44,66,87],{"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":27,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":43},"100634938","ai-based-predictive-and-interventional-system-for-early-detection-of-non-compliance-risks-with-oral-therapies-in-lymphoma-patients-100634938",false,"NCT07546188","AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients.","AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients, Integrating the Complete Care Pathway and an Interoperable Clinical Interface With Algorithms Paired With Explainability Tools.","LNH-AI-Tools","Inclusion Criteria:\n\n* All patients aged 18 and over who are treated in the Haematology Department at the Grand Hôpital de Charleroi from November 2025 onwards\n* Treated for a lymphoma, Non Hodgkin\n* Capable of giving informed consent\n\nExclusion Criteria:\n\n* All other patients who did not meet the eligibility criteria","ALL","18 Years",{"count":20,"type":21},210,"ESTIMATED","OBSERVATIONAL","This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions.",[25,26],"Lymphoma, Non-Hodgkin","Care Coordination",[28,29,30],"Artificial intelligence","Non-Hodgkin lymphoma","Care pathway","RECRUITING","2026-04-20",{"date":34,"type":35},"2026-04-22","ACTUAL",{"date":37,"type":35},"2026-02-15",{"date":39,"type":21},"2029-02-15",{"name":41,"class":42},"Grand Hôpital de Charleroi","OTHER",1,{"id":45,"slug":46,"hasResults":11,"nctId":47,"briefTitle":48,"officialTitle":48,"acronym":4,"eligibilityCriteria":49,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":50,"targetDuration":4,"studyType":52,"phases":53,"briefSummary":55,"conditions":56,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":58,"lastUpdatePostDateStruct":59,"startDateStruct":61,"completionDateStruct":63,"leadSponsor":65,"locationsCount":43},"100571291","assessing-the-japetw-lumbar-traction-device-in-rehabilitation-for-patients-with-non-specific-mechanical-low-back-pain-100571291","NCT06718348","Assessing the Japet.W+ Lumbar Traction Device in Rehabilitation for Patients With Non-specific Mechanical Low Back Pain","Inclusion Criteria:\n\n* Nonspecific mechanical low back pain for more than 6 weeks,\n* 18 years ≤ Age \\\u003C 80 years,\n* Patient affiliated to the health social security system,\n* Patient able to understand the information relating to the study and to sign the informed consent form\n\nExclusion Criteria:\n\n* Specific low back pain (infectious, inflammatory, tumorous, or traumatic origin),\n* Motor neurological deficits (peripheral or central),\n* Extrapyramidal syndrome,\n* Treatment with implanted neurostimulation,\n* Cardiac or circulatory diseases or serious respiratory problems,\n* Previous recent arthrodesis or recent lumbar prosthesis surgery,\n* Surgery for herniated disc (\\\u003C3 months),\n* Fracture of the dorsolumbar spine or rib (\\\u003C3 months),\n* Skin lesions, contusions and stretching injuries of the trunk,\n* Pregnancy,\n* Individuals not tolerating the JAPET.W+ device during an initial trial,\n* Under guardianship, curatorship or legal protection",{"count":51,"type":21},60,"INTERVENTIONAL",[54],"NA","The study will include patients suffering from nonspecific mechanical low back pain for more than 6 weeks. Two groups will be compared. A Control group and an Experimental group with the exoskeleton JAPET.W",[57],"Low Back Pain, Mechanical","2026-04-14",{"date":60,"type":35},"2026-04-17",{"date":62,"type":35},"2025-12-20",{"date":64,"type":21},"2026-12-31",{"name":41,"class":42},{"id":67,"slug":68,"hasResults":11,"nctId":69,"briefTitle":70,"officialTitle":70,"acronym":71,"eligibilityCriteria":72,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":73,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":75,"conditions":76,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":81,"completionDateStruct":83,"leadSponsor":85,"locationsCount":86},"100559871","evolution-of-the-patients-perception-of-the-use-of-the-upper-limb-after-a-stroke-during-intensive-rehabilitation-100559871","NCT06569771","Evolution of the Patient's Perception of the Use of the Upper Limb After a Stroke During Intensive Rehabilitation","ValetM-2024","Inclusion Criteria:\n\n* First stroke as defined by the World Health Organization.\n* Upper limb paresis with a NIHSS National Institutes of Health Stroke Scale score \\> or = 1\u002F4 for upper limb motor function\n\nExclusion Criteria:\n\n* Life expectancy \\\u003C12 months\n* Inability to understand French.\n* Pre-existing severe multiple disability affecting arm function.\n* Presence of pre-existing central neurological pathologies.\n* Mini-Mental State Examination \\\u003C 21",{"count":74,"type":21},100,"This study is being carried out in the context of rehabilitation after stroke, where persistent upper limb weakness is a major challenge. It aims to explore the relationship between observed function and patients' perceived participation in use.",[77],"Upper Limb Ischemia","2026-04-08",{"date":80,"type":35},"2026-04-13",{"date":82,"type":35},"2024-06-11",{"date":84,"type":21},"2027-12-31",{"name":41,"class":42},2,{"id":88,"slug":89,"hasResults":11,"nctId":90,"briefTitle":91,"officialTitle":92,"acronym":93,"eligibilityCriteria":94,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":95,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":97,"conditions":98,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":100,"lastUpdatePostDateStruct":101,"startDateStruct":103,"completionDateStruct":105,"leadSponsor":106,"locationsCount":43},"100534661","prospective-observational-study-of-diffuse-large-cell-b-lymphoma-100534661","NCT06241729","Prospective Observational Study of Diffuse Large-cell B Lymphoma","Supervised Machine Learning for the Prediction of Primary Refractory Status in Patients With Diffuse Large Cell B Lymphoma in a Monocentric Cohort at the Grand Hôpital de Charleroi","LBDGCréfract","Inclusion Criteria:\n\n* patients with diffuse large-cell B lymphoma treated in the haematology department at the Grand Hôpital de Charleroi for the first time\n* able to understand the information and sign their consent form\n\nExclusion Criteria:\n\n* under 18 years old",{"count":96,"type":21},50,"Diffuse large B-cell lymphoma (DLBCL) represents the most common type of non-Hodgkin lymphoma and is currently a curable malignant disease for many patients with immuno-chemotherapy frontline treatment. However, around 30-40 % of patients, are unresponsive or will experience early relapse. The prognosis of primary refractory patient is poor and the management and treatment are a significant challenge due to the disease heterogeneity and the complex genetic framework. The reasons for refractoriness are various and include genetic abnormalities, alterations in tumor and tumor microenvironment. Patient related factors such as comorbidities can also influence treatment outcome. Recently the progress in Machine learning (ML) showed its usefulness in the procedures used to analyze large and complex datasets. In medicine, machine learning is used to create some predictive tools based on data-driven analytic approach and integration of various risk factors and parameters. Machine learning, as a subdomain of artificial intelligence (AI), has the capability to autonomously uncover patterns within datasets. It offers algorithms that can learn from examples to perform a task automatically.The investigators tested in a previous study five machine learning algorithms to establish a model for predicting the risk of primary refractory DLBCL using parameters obtained from a monocentric dataset. The investigators observed that NB Categorical classifier was the best alternative for building a model in order to predict primary refractory disease in DLBCL patients and the second was XGBoost.The investigators plan to extend this previous study by further exploring the two best-performing models (NBC Classifier and XGBoost), progressively incorporating a larger number of patients in a prospective way.",[99],"Lymphoma, B-Cell","2025-07-30",{"date":102,"type":35},"2025-07-31",{"date":104,"type":35},"2023-01-03",{"date":64,"type":21},{"name":41,"class":42},""]