[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100578310":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":25,"centralContacts":30,"locations":36,"responsibleParty":55,"collaborators":25,"id":59,"slug":60,"hasResults":61,"nctId":62,"briefTitle":63,"officialTitle":64,"acronym":25,"eligibilityCriteria":65,"healthyVolunteers":66,"sex":67,"minAge":68,"maxAge":69,"enrollmentInfo":70,"targetDuration":25,"studyType":73,"phases":74,"briefSummary":76,"conditions":77,"keywords":80,"overallStatus":39,"whyStopped":25,"lastUpdateSubmitDate":85,"lastUpdatePostDateStruct":86,"startDateStruct":89,"completionDateStruct":91,"leadSponsor":93,"locationsCount":94},{"fullName":5,"class":6},"Neuron, Spain","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"LLM group","EXPERIMENTAL","Participants in the experimental group will undergo an 8-week intervention incorporating Large Language Model (LLM)-based training into their clinical reasoning education.\n\nStudents will engage in weekly clinical case simulations using an LLM-powered platform (ChatGPT), where they will interact with the model to obtain patient information, formulate diagnoses, and propose treatment plans. The LLM will provide real-time responses, simulating a virtual patient encounter.\n\nThe training will complement the standard curriculum, allowing students to practice clinical reasoning skills in a structured and interactive AI-assisted environment. At the end of the intervention, participants will complete a final case-based assessment to evaluate improvements in clinical reasoning, digital competence, and engagement with the technology.",[13],"Other: Large Language Model",{"label":15,"type":16,"description":17,"interventionNames":18},"Conventional learning group","ACTIVE_COMPARATOR","Participants in the control group will follow the standard curriculum for clinical reasoning training over an 8-week period, without exposure to the LLM-based intervention.\n\nStudents will engage in weekly case-based discussions using traditional learning methods, including written case analyses and supervised discussions with instructors. These sessions will follow the usual educational framework used in physical therapy training programs, emphasizing diagnostic reasoning and treatment planning through instructor-led guidance.\n\nAt the end of the training period, participants will complete a final case-based assessment to evaluate their clinical reasoning skills, digital competence, and overall engagement with the learning process.",[19],"Other: Conventional",[21,26],{"type":6,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"Large Language Model","The intervention in the experimental group is distinguished by the integration of a Large Language Model (LLM)-based interactive platform (ChatGPT) into clinical reasoning training for physical therapy students. Unlike traditional educational approaches, this intervention provides real-time, AI-generated patient interactions, allowing students to actively engage in virtual clinical case simulations.",[9],null,{"type":6,"name":27,"description":28,"armGroupLabels":29,"otherNames":25},"Conventional","The intervention in the control group follows a traditional case-based learning approach, which is commonly used in physical therapy education. Unlike the experimental group, this training method relies solely on human-led instruction and written case analysis, without the integration of artificial intelligence or interactive digital tools.",[15],[31],{"name":32,"role":33,"phone":34,"phoneExt":25,"email":35},"Alfredo Lerín Calvo, Professor","CONTACT","+34620187457","alfredo.lerin@lasallecampus.es",[37],{"facility":38,"status":39,"city":40,"state":40,"zip":41,"country":42,"countryCode":43,"cosmosGeoPoint":44,"geoPoint":49,"contacts":50},"Centro Superior de Estudios Universitarios La Salle","RECRUITING","Madrid","28023","Spain","ES",{"type":45,"coordinates":46},"Point",[47,48],-3.70256,40.4165,{"lat":48,"lon":47},[51,52],{"name":32,"role":33,"phone":34,"phoneExt":25,"email":35},{"name":53,"role":54,"phone":25,"phoneExt":25,"email":25},"Raúl Ferrer Peña, PhD","PRINCIPAL_INVESTIGATOR",{"type":54,"investigatorFullName":56,"investigatorTitle":57,"investigatorAffiliation":58,"oldNameTitle":25,"oldOrganization":25},"Alfredo Lerín Calvo","Professor","Centro Universitario La Salle","100578310","phase-2-large-linguistic-model-for-clinical-reaoning-of-physical-therapy-students-100578310",false,"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.",true,"ALL","18 Years","30 Years",{"count":71,"type":72},60,"ESTIMATED","INTERVENTIONAL",[75],"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.",[78,79],"Healthy","Artificial Intelligence (AI)",[81,82,83,84],"Clinical reasoning","Physical Therapy education","Artificial Intelligence","Large Language Models","2025-12-03",{"date":87,"type":88},"2025-12-04","ACTUAL",{"date":90,"type":88},"2023-09-01",{"date":92,"type":72},"2026-07-30",{"name":5,"class":6},1]