[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100631925":3},{"organization":4,"outcomesModule":7,"designInfo":30,"detailedDescription":38,"studyPopulation":29,"armGroups":39,"interventions":52,"overallOfficials":62,"centralContacts":70,"locations":76,"responsibleParty":97,"collaborators":29,"id":101,"slug":29,"hasResults":102,"nctId":103,"briefTitle":104,"officialTitle":105,"acronym":106,"eligibilityCriteria":107,"healthyVolunteers":108,"sex":109,"minAge":110,"maxAge":29,"enrollmentInfo":111,"targetDuration":29,"studyType":114,"phases":115,"briefSummary":117,"conditions":118,"keywords":120,"overallStatus":128,"whyStopped":29,"lastUpdateSubmitDate":129,"lastUpdatePostDateStruct":130,"startDateStruct":133,"completionDateStruct":135,"leadSponsor":137,"locationsCount":138},{"fullName":5,"class":6},"Universitat de Lleida","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":29},[9],{"measure":10,"description":11,"timeFrame":12},"Clinical adequacy score (0-100 rubric)","Clinical adequacy will be assessed using a validated 0-100 rubric applied to standardized clinical cases. Scores range from 0 (lowest adequacy) to 100 (highest adequacy), and higher scores indicate a better clinical outcome. The rubric evaluates three domains: (1) evaluation\u002Ftriage (appropriate tests and red-flag assessment), (2) therapeutic recommendation (prioritizing active strategies and evidence-based pain education), and (3) clinical communication (avoiding iatrogenic messages and establishing functional goals). Scores are generated by double-blinded assessors with adjudication by a third reviewer in case of disagreement (target kappa ≥0.70). Change will be analyzed from T0 to T1 and T2.","Baseline (T0), immediate post-training (T1), and 3-month follow-up (T2)",[14,18,22,26],{"measure":15,"description":16,"timeFrame":17},"System Usability Scale (SUS, 0-100)","Usability of the PainTrain-AI intervention will be assessed with the System Usability Scale (SUS), a validated 10-item questionnaire that produces a score from 0 (lowest usability) to 100 (highest usability). Higher scores indicate better usability. The feasibility threshold is SUS ≥70, with an optimal target ≥80.","Immediately after training",{"measure":19,"description":20,"timeFrame":21},"Adherence to PainTrain-AI micromodules (% completed)","Adherence will be measured as the percentage of scheduled micromodules or simulation sessions completed by each participant within the defined training period. Scores range from 0% (no modules completed) to 100% (all assigned modules completed). Higher percentages indicate greater engagement with the behavioral intervention.","During the training period (up to 4 weeks)",{"measure":23,"description":24,"timeFrame":25},"Low-value clinical practice indicators (count per case)","Low-value practice indicators will be measured using standardized clinical cases scored with the 0-100 adequacy rubric. For this outcome, low-value practices are counted as discrete events, with possible values ranging from 0 (no low-value decisions in a case) to a higher count indicating more inappropriate decisions. Indicators include inappropriate diagnostic imaging, unnecessary pharmacologic escalation, or early referral to specialized care when not clinically indicated. The measure reflects the number of low-value decisions per case at each time point.","Baseline, immediately after training, and 3 months after training",{"measure":27,"description":28,"timeFrame":21},"System latency (seconds)","Latency will be measured as the average system response time (in seconds) between user input and platform output during PainTrain-AI interactions. Scores range from 0 seconds (best possible performance) upward, with higher values indicating greater technological friction and lower technical performance. Latency is automatically recorded through system logs.",null,{"allocation":31,"interventionModel":32,"interventionModelDescription":33,"primaryPurpose":34,"observationalModel":29,"timePerspective":29,"maskingInfo":35},"NON_RANDOMIZED","PARALLEL","Cluster-assigned parallel design in which entire Primary Care centers are allocated to either the intervention arm (PainTrain-AI) or the control arm (usual training\u002Fstandard practice). Cluster allocation prevents contamination between groups. Outcomes are measured at T0, T1, and T2 for all participants within each cluster.","HEALTH_SERVICES_RESEARCH",{"masking":36,"maskingDescription":37,"whoMasked":29},"NONE","No participant or investigator masking is applied. Outcome assessors scoring the 0-100 clinical adequacy rubric are blinded to arm allocation and timepoint, with adjudication by a third reviewer in case of disagreement.","This study is a pragmatic, cluster-assigned implementation trial designed to evaluate the impact of the digital educational intervention PainTrain-AI on clinical decision-making in Primary Care. The intervention trains health professionals through standardized virtual patient simulations and brief interactive micromodules, using a retrieval-augmented generation (RAG) safety architecture that restricts all outputs to validated clinical content. PainTrain-AI does not generate new clinical information and is used exclusively for educational purposes.\n\nThe trial corresponds to Phase 3 of a broader research program described in the project protocol. In this phase, entire Primary Care centers are allocated to either the intervention arm (PainTrain-AI training) or the control arm (usual training\u002Fstandard practice). A natural comparative setting in specialized care is included for analytical purposes, without access to master datasets and without receiving any financial support. The primary outcome is clinical adequacy, assessed through a validated 0-100 rubric applied to standardized clinical cases at three time points: baseline (T0), immediate post-training (T1), and follow-up (T2). Double-blind scoring and a third-reviewer arbitration process are used to ensure reliability (target kappa ≥0.70).\n\nSecondary outcomes include changes in validated psychometric measures (NPQ-12, PABS-PT, HC-PAIRS, FABQ), adherence to PainTrain-AI micromodules, system usability (SUS), latency\u002Ffriction metrics derived from platform logs, and indicators of low-value clinical practices. The trial incorporates a sex\u002Fgender randomization of clinical vignettes to identify and analyze potential gender bias in clinical decisions. All data collected refer to professionals, not patients. No identifiable clinical data are used; all datasets are pseudonymized according to the approved Data Management Plan (FAIR).\n\nAssignment by clusters prevents contamination between arms, and all centers follow standardized operating procedures (PNT) for recruitment, training, and evaluation. The platform maintains audit logs, human-in-the-loop oversight, and ENS\u002FRGPD-compliant security measures. Analyses will use mixed-effects models and Difference-in-Differences approaches to account for clustering, repeated measures, and baseline differences. Results will inform the feasibility, acceptability, and preliminary effectiveness of a digital simulation-based educational intervention intended to reduce the competency gap, decrease low-value practices, and mitigate gender bias in the management of chronic musculoskeletal pain in Primary Care.",[40,46],{"label":41,"type":42,"description":43,"interventionNames":44},"PainTrain-AI Educational Training (Cluster Implementation Arm)","EXPERIMENTAL","Participants in Primary Care centers allocated to the intervention cluster receive the PainTrain-AI behavioral educational training. The intervention consists of simulated clinical encounters with virtual standardized patients and interactive micromodules, using a RAG-fenced architecture restricted to validated clinical content. All participants in this arm complete T0, T1, and T2 assessments.",[45],"Behavioral: PainTrain-AI Educational Training",{"label":47,"type":48,"description":49,"interventionNames":50},"Usual Training \u002F Standard Practice (Control Arm)","ACTIVE_COMPARATOR","Participants in Primary Care centers allocated to the control cluster follow usual training and standard practice available at their institution. No exposure to the PainTrain-AI platform occurs during the study period. Centers continue their routine continuing medical education activities. All participants complete T0-T1-T2 assessments with the same standardized clinical cases and rubric. This arm serves as the active comparator for evaluating changes in clinical adequacy, low-value practice indicators, and gender-related decision patterns.",[51],"Behavioral: Usual Training \u002F Standard Practice",[53,58],{"type":54,"name":55,"description":56,"armGroupLabels":57,"otherNames":29},"BEHAVIORAL","PainTrain-AI Educational Training","PainTrain-AI is a behavioral educational intervention delivered through a digital clinical simulation platform. The system uses a retrieval-augmented generation (RAG) safety architecture that restricts all outputs to validated clinical content; the platform is non-diagnostic and does not generate new clinical information. Participants complete simulated consultations with virtual standardized patients and a series of brief micromodules designed to train biopsychosocial clinical reasoning for chronic musculoskeletal pain. The intervention is used exclusively for professional training and does not involve patient data. All participants in the intervention arm complete assessments at T0, T1, and T2.",[41],{"type":54,"name":59,"description":60,"armGroupLabels":61,"otherNames":29},"Usual Training \u002F Standard Practice","Participants continue with usual training and standard practice available at their institution.\n\nNo exposure to PainTrain-AI occurs. This arm serves as the active comparator.",[47],[63,67],{"name":64,"affiliation":65,"role":66},"Fran Valenzuela-Pascual, PhD","IRBLleida \u002F Universitat de Lleida","STUDY_DIRECTOR",{"name":68,"affiliation":69,"role":66},"Ester García Martínez, PhD","Universitat de Lleida \u002F IDIAP Jordi Gol",[71],{"name":72,"role":73,"phone":74,"phoneExt":29,"email":75},"Fran Valenzuela Pascual, PhD","CONTACT","+34 973 702 459","fran.valenzuela@udl.cat",[77,90],{"facility":78,"status":29,"city":79,"state":79,"zip":29,"country":80,"countryCode":81,"cosmosGeoPoint":82,"geoPoint":87,"contacts":88},"Institut Català de la Salut (ICS) - Primary Care Network","Lleida","Spain","ES",{"type":83,"coordinates":84},"Point",[85,86],0.62218,41.61674,{"lat":86,"lon":85},[89],{"name":72,"role":73,"phone":74,"phoneExt":29,"email":75},{"facility":91,"status":29,"city":79,"state":79,"zip":29,"country":80,"countryCode":81,"cosmosGeoPoint":92,"geoPoint":94,"contacts":95},"Specialized Care Comparative Setting",{"type":83,"coordinates":93},[85,86],{"lat":86,"lon":85},[96],{"name":72,"role":73,"phone":74,"phoneExt":29,"email":75},{"type":98,"investigatorFullName":99,"investigatorTitle":100,"investigatorAffiliation":5,"oldNameTitle":29,"oldOrganization":29},"SPONSOR_INVESTIGATOR","Francesc Valenzuela Pascual","Principal Investigator (Sponsor-Investigator)","100631925",false,"NCT07507019","PainTrain-AI Implementation Trial in Primary Care (Competency Gaps and Gender Bias)","Implementation of the Digital Intervention PainTrain-AI in Primary Care: Competency Gaps and Gender Bias in the Management of Chronic Musculoskeletal Pain","PT-AI-IMP","Inclusion Criteria:\n\n* Health professionals (medicine, nursing, physiotherapy, psychology, nutrition) working at participating Primary Care centers.\n* Active clinical practice during the study period.\n* Ability to access and use the digital platform (PainTrain-AI) and complete online assessments (T0, T1, T2).\n* Provides informed consent.\n\nExclusion Criteria:\n\n* Prior exposure to the PainTrain-AI platform within the previous 12 months (risk of contamination).\n* Planned absence or temporary leave during the evaluation windows (T0, T1, T2).\n* Inability to use the digital platform for technical reasons not solvable by support.\n* Declines participation.",true,"ALL","18 Years",{"count":112,"type":113},185,"ESTIMATED","INTERVENTIONAL",[116],"NA","Cluster trial to assess the effect of a digital clinical simulation (PainTrain AI) on clinical adequacy (0-100 rubric) across T0-T1-T2 in Primary Care. Secondary endpoints: SUS, adherence, low value practice indicators, and latency\u002Ffriction. The intervention is educational\u002Fbehavioral; the platform is non diagnostic and RAG fenced to validated content. Analysis per protocol: DiD and LMM\u002FGLMM.",[119],"Educational Intervention for Primary Care Professionals on Chronic Musculoskeletal Pain",[121,122,123,124,125,126,127],"chronic musculoskeletal pain","clinical simulation","digital training","primary care professionals","educational intervention","usability SUS","concept shift","NOT_YET_RECRUITING","2026-03-28",{"date":131,"type":132},"2026-04-02","ACTUAL",{"date":134,"type":113},"2029-03",{"date":136,"type":113},"2030-12",{"name":99,"class":6},2]