{"id":"W2486118470","doi":"10.14288/1.0075871","title":"Kinecting the moves: the kinematic potential of rehabilitation-specific gaming to inform treatment for hemiplegia","year":2015,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rehabilitation; Kinematics; Motion capture; Physical medicine and rehabilitation; Computer science; Virtual reality; Hemiparesis; Human–computer interaction; Motion (physics); Psychology; Physical therapy; Artificial intelligence; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000369572,0.0004312317,0.0002868825,0.0005068625,0.0001121815,0.0004882877,0.0002595284,0.0002789941,0.002950964],"category_scores_gemma":[0.0009812579,0.0001454843,0.0001350401,0.0003978909,0.0001560566,0.0004448421,0.0004708814,0.0001751681,0.0005958493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374123,"about_ca_system_score_gemma":0.0001882623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001711547,"about_ca_topic_score_gemma":0.007747244,"domain_scores_codex":[0.9998363,0.0000398353,0.00001309737,0.00003377665,0.00006474262,0.00001224389],"domain_scores_gemma":[0.9998521,0.00006037005,0.00002991973,0.000007178606,0.0000367012,0.00001379079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003669656,0.0002725074,0.05009296,0.001799655,0.0002225709,0.0004723025,0.0009660249,0.003779806,0.2094197,0.002531765,0.007382325,0.7193909],"study_design_scores_gemma":[0.0003967862,0.003247724,0.7650197,0.0008908415,0.0005650487,0.006292708,0.001411497,0.08023138,0.09835298,0.004959894,0.03831762,0.0003139803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7776209,0.009541093,0.1659908,0.0007561928,0.000310384,0.0007346525,0.009648959,0.002466307,0.03293073],"genre_scores_gemma":[0.9030505,0.002166824,0.08350749,0.0003035259,0.00006412655,0.0003143073,0.001359796,0.0001436966,0.009089677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002950964,"threshold_uncertainty_score":0.00987196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047956853883102,"score_gpt":0.2249535134731821,"score_spread":0.2044739449343511,"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."}}