{"id":"W2192870960","doi":"10.2196/rehab.4776","title":"Are Virtual Rehabilitation Technologies Feasible Models to Scale an Evidence-Based Fall Prevention Program? A Pilot Study Using the Kinect Camera","year":2015,"lang":"en","type":"article","venue":"JMIR Rehabilitation and Assistive Technologies","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention","keywords":"Rehabilitation; Scale (ratio); Computer science; Physical medicine and rehabilitation; Simulation; Human–computer interaction; Artificial intelligence; Medicine; Physical therapy; Cartography; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0142817,0.0005457849,0.0006606476,0.0008716571,0.0009337096,0.001231699,0.0008921233,0.001027982,0.003528487],"category_scores_gemma":[0.0217287,0.0007050625,0.0009631682,0.0003669703,0.0007102878,0.002056653,0.001328931,0.0009880936,0.0004167412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006418503,"about_ca_system_score_gemma":0.001737805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001517224,"about_ca_topic_score_gemma":0.004530939,"domain_scores_codex":[0.9939638,0.004119354,0.000464707,0.0002624686,0.000757353,0.0004323275],"domain_scores_gemma":[0.9874743,0.006958985,0.001161896,0.0006821315,0.002459866,0.001262816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.02044179,0.2663914,0.2147873,0.00824878,0.0006530859,0.00215545,0.05850715,0.001139851,0.02612899,0.001201603,0.005307372,0.3950372],"study_design_scores_gemma":[0.00906056,0.5908687,0.3024336,0.001956406,0.0008000607,0.00102689,0.06874184,0.003035657,0.006292026,0.000584534,0.01497977,0.0002200065],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914675,0.0002047102,0.001390619,0.0002911082,0.00005020258,0.005392155,0.00008842268,0.00001516907,0.001100169],"genre_scores_gemma":[0.9739595,0.0005972429,0.01337217,0.0004303113,0.00004984094,0.01055412,0.0001234583,0.000009710595,0.0009038022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0142817,"threshold_uncertainty_score":0.07552975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1511278382690238,"score_gpt":0.4326279211910989,"score_spread":0.2815000829220751,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}