[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Neuron, Spain\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":103},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,46,75],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":23,"studyType":24,"phases":4,"briefSummary":25,"conditions":26,"keywords":30,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":34,"lastUpdatePostDateStruct":35,"startDateStruct":38,"completionDateStruct":40,"leadSponsor":42,"locationsCount":45},"100645000","large-language-models-versus-human-examiners-for-grading-physiotherapy-clinical-cases-100645000",false,"NCT07677202","Large Language Models Versus Human Examiners for Grading Physiotherapy Clinical Cases","Agreement Between Large Language Models and Faculty Assessment in the Evaluation of Clinical Reasoning Case Examinations in Undergraduate Physiotherapy Education: A Comparative Reliability Study","PACE-AI","Inclusion Criteria:\n\n* Students officially enrolled in the course \"Specific Methods in Physiotherapy\" (third year of the Physiotherapy Degree) during the study period.\n* Submission of a completed written clinical-reasoning case examination as part of the course.\n* Provision of informed consent for the anonymized examination to be used for educational-research purposes.\n\nExclusion Criteria:\n\n* Refusal to provide, or withdrawal of, informed consent.\n* Blank, incomplete, or non-evaluable examinations (e.g., no developed written response).\n* Examinations that cannot be reliably de-identified prior to assessment.",true,"ALL","18 Years",{"count":21,"type":22},65,"ESTIMATED","1 Day","OBSERVATIONAL","This study evaluates whether large language models (LLMs) can reliably assess written clinical-reasoning case examinations completed by undergraduate physiotherapy students, compared with faculty assessment. In the course \"Specific Methods in Physiotherapy\" (third year of the Physiotherapy Degree), students solve complex clinical cases that require clinical reasoning, technical knowledge, and therapeutic decision-making. These cases are traditionally graded by faculty, a time-consuming process that may show inter-rater variability.\n\nA set of de-identified student case examinations will be assessed using the rubric currently applied in the course, which covers clarity and structure of clinical reasoning, integration of the biopsychosocial model (ICF and APTA frameworks), accuracy in identifying pain mechanisms, coherence between diagnosis, hypotheses, and treatment, originality and depth of analysis, and professional writing. Each examination will be scored independently by three LLMs (for example, Claude, ChatGPT, and Gemini), each receiving an identical standardized prompt that embeds the same rubric, and by faculty serving as the reference standard.\n\nTo avoid overloading faculty, full double human grading may not be feasible; the human reference will therefore consist of expert faculty grading by one independent rater or, when resources allow, two independent raters. In contrast, paired assessment is fully implemented across the AI models: each examination is scored by several LLMs, and each model is queried in duplicate, allowing the study to estimate agreement between models and the test-retest stability of each model.\n\nThe primary aim is to quantify agreement between LLM-generated scores and the faculty reference score. Secondary aims include agreement among the LLMs, test-retest reliability of each model, criterion-level agreement, the quality and usefulness of the qualitative feedback generated, the time and cost associated with each approach, and students' perceptions of the usefulness of human versus AI feedback.\n\nThe findings will clarify the strengths and limitations of LLMs as supportive tools for formative assessment in health-professions education and will inform criteria for their responsible and effective use. No LLM output will affect students' official grades, which remain the sole responsibility of faculty.",[27,28,29],"Educational Assessment","Artifical Intelligence","Physical Therapy Education",[31,32],"Clinical Competence","Educational, Medical","NOT_YET_RECRUITING","2026-06-24",{"date":36,"type":37},"2026-06-30","ACTUAL",{"date":39,"type":22},"2026-08-01",{"date":41,"type":22},"2026-08-10",{"name":43,"class":44},"Neuron, Spain","OTHER",1,{"id":47,"slug":48,"hasResults":11,"nctId":49,"briefTitle":50,"officialTitle":50,"acronym":4,"eligibilityCriteria":51,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":52,"targetDuration":4,"studyType":54,"phases":55,"briefSummary":57,"conditions":58,"keywords":62,"overallStatus":65,"whyStopped":4,"lastUpdateSubmitDate":66,"lastUpdatePostDateStruct":67,"startDateStruct":69,"completionDateStruct":71,"leadSponsor":73,"locationsCount":74},"100618128","exercise-induced-hypoalgesia-in-pain-free-stroke-and-healthy-populations-a-cohort-study-100618128","NCT07327593","Exercise Induced Hypoalgesia in Pain-free Stroke and Healthy Populations: a Cohort Study","Inclusion Criteria:\n\nHealthy cohort:\n\n* Age \\> 18 years\n* No neurological damage\n* No medical conditions that could affect the test\n\nPost-stroke cohort:\n\n* Age \\> 18 years\n* Ischemic or hemorrhagic stroke (time since onset \\> 3 months)\n\nExclusion Criteria:\n\nHealthy cohort:\n\n* Any medical condition that could affect the test (respiratory, cardiovascular, metabolic diseases, for example)\n* Pain in any location, of duration \\> 1 week in the 3 months preceding the trial.\n\nPost-stroke cohort:\n\n* Unable to participate in the test (no possibility of transfer or active pedaling)\n* Medical contraindication to cardiovascular training for any reason\n* Inability to follow instructions or communicate sensations during training\n* Other neurological pathology, cardiac pathology (unstable angina, arrhythmias, aortic or left ventricular problems, myocarditis, pericarditis), pulmonary pathology that affects the test (pulmonary embolisms, pulmonary hypertension, etc.), renal or metabolic problems (severe renal dysfunction, uncontrolled diabetes, etc.), acute infection or fever, neurosurgery within 6 weeks prior to inclusion in the study\n* Pain in any location, lasting \\> 1 week in the 3 months prior to the trial.\n* Use of beta-blockers, antidepressants, or any medication that may alter heart rate measurements",{"count":53,"type":22},60,"INTERVENTIONAL",[56],"NA","Exercise has shown multiple beneficial effects in both healthy and post-stroke populations. One of these is the acute reduction in sensitivity to painful stimuli, called exercise-induced hypoalgesia (EIH). This phenomenon has been studied since 1979 and has shown improvements in pain thresholds with both aerobic and resistance training in healthy, pain-free populations and different chronic pain conditions.\n\nAlthough there has been extensive research on EIH in healthy populations and those with chronic musculoskeletal pain, surprisingly little attention has been given to individuals with neurological pathologies. Chronic pain is found in more than 50% of patients after stroke, and 70% of affected individuals experience pain on daily activities. Reported prevalences of post-stroke pain (PSP) between different studies, but there is a general consensus that it is an underreported phenomenon. Patients with pain experience greater cognitive and functional decline, fatigue, depression and lower quality of life.\n\nMultiple factors contribute to PSP, and various approaches exist to treat all the variables influencing it. This study aims to compare the effects of exercise on pain perception in healthy individuals and stroke patients without pain, using the same cardiovascular training protocol, to better understand the mechanisms of EIH and its maintenance after stroke, ultimately aiming to improve the treatment of people with stroke.",[59,60,61],"Stroke","Pain","Exercise Induced Hypoalgesia",[59,60,63,64],"Exercise induced hypoalgesia","Cardiovascular training","RECRUITING","2026-03-17",{"date":68,"type":37},"2026-03-20",{"date":70,"type":22},"2026-04",{"date":72,"type":22},"2026-11",{"name":43,"class":44},2,{"id":76,"slug":77,"hasResults":11,"nctId":78,"briefTitle":79,"officialTitle":80,"acronym":4,"eligibilityCriteria":81,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":82,"enrollmentInfo":83,"targetDuration":4,"studyType":54,"phases":84,"briefSummary":86,"conditions":87,"keywords":90,"overallStatus":65,"whyStopped":4,"lastUpdateSubmitDate":95,"lastUpdatePostDateStruct":96,"startDateStruct":98,"completionDateStruct":100,"leadSponsor":102,"locationsCount":45},"100578310","phase-2-large-linguistic-model-for-clinical-reaoning-of-physical-therapy-students-100578310","NCT06809634","Large Linguistic Model for Clinical Reaoning of Physical Therapy Students","Feasibility of a Randomized Controlled Trial of Large Artificial Intelligence-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students. A Randomized Controlled Trial","Inclusion Criteria:\n\n* Students enrolled in the third year of the Physiotherapy program at La Salle Centre for Higher University Studies (LCHUS)\n* Participants must be between 18 and 30 years old.\n* Students must agree to participate in the study by signing an informed consent form after being briefed about the study's objectives, procedures, and potential risks.\n* Participants must be willing to engage with the LLM-based platform (for the experimental group) or participate in traditional learning activities (for the control group) for the duration of the study.\n\nExclusion Criteria:\n\n* Students with previous clinical experience beyond the third year of physiotherapy education.\n* Physical or cognitive disabilities that may interfere with the ability to participate in or benefit from the intervention (e.g., vision, hearing, or motor impairments).\n* Students who do not provide informed consent to participate in the study.\n* Students who do not possess sufficient proficiency in Spanish or English to understand the materials and the intervention.","30 Years",{"count":53,"type":22},[85],"PHASE2","Clinical reasoning is a fundamental skill for physical therapy students, enabling them to collect and interpret patient information to make accurate diagnoses and treatment decisions. Traditional training methods often limit students' exposure to a diverse range of clinical cases, which can restrict the development of these skills. The integration of Large Language Models (LLMs), such as ChatGPT, into physical therapy education offers a novel approach to enhance clinical reasoning by simulating interactive and realistic patient scenarios.\n\nThis randomized controlled trial aims to evaluate the effectiveness of an LLM-based educational intervention in improving clinical reasoning skills in physical therapy students. The study will recruit a total of 200 third-year physiotherapy students from multiple university institutions. Participants will be randomly assigned to one of two groups:\n\n1. Experimental Group - Students will receive LLM-based training, engaging with a conversational artificial intelligence model to solve clinical cases over an 8-week period. The model will provide real-time responses to their questions, allowing them to refine their diagnostic and treatment reasoning.\n2. Control Group - Students will follow the standard curriculum, participating in conventional case-based learning and supervised clinical reasoning exercises without AI-based assistance.\n\nThe primary outcome of the study is the improvement in clinical reasoning skills, assessed through standardized written case evaluations and structured practical examinations. Secondary outcomes include changes in digital competence, student engagement levels, overall satisfaction with the educational approach, and cost-effectiveness of the intervention.\n\nBy assessing the impact of LLMs on clinical reasoning training, this study seeks to determine whether AI-driven educational tools can effectively complement traditional physiotherapy education and improve student preparedness for real-world clinical practice.",[88,89],"Healthy","Artificial Intelligence (AI)",[91,92,93,94],"Clinical reasoning","Physical Therapy education","Artificial Intelligence","Large Language Models","2025-12-03",{"date":97,"type":37},"2025-12-04",{"date":99,"type":37},"2023-09-01",{"date":101,"type":22},"2026-07-30",{"name":43,"class":44},""]