{"id":"W3198532616","doi":"10.3390/app11177986","title":"An Exergame Solution for Personalized Multicomponent Training in Older Adults","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Active and Assisted Living programme; European Commission","keywords":"Personalization; Psychological intervention; Wearable computer; Urinary incontinence; Balance (ability); Applied psychology; Cognition; Rehabilitation; Physical medicine and rehabilitation; Session (web analytics); Psychology; Human–computer interaction; Physical therapy; Computer science; 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":[0.0002602023,0.0006326549,0.0002481073,0.000388191,0.0001642569,0.0002622466,0.000500276,0.0004864515,0.007009106],"category_scores_gemma":[0.0005467213,0.0001460197,0.0005264429,0.0001085563,0.0001036957,0.0003776331,0.001009093,0.0003125801,0.001876684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009070253,"about_ca_system_score_gemma":0.0002769589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004485552,"about_ca_topic_score_gemma":0.001020037,"domain_scores_codex":[0.9998878,0.00002508577,0.00001098266,0.00002156321,0.00003459324,0.00001994994],"domain_scores_gemma":[0.9999336,0.00001676087,0.000005225474,0.000004296411,0.00001550183,0.00002455907],"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":[0.001805739,0.005946775,0.005487658,0.002305407,0.0002339292,0.001260069,0.001210185,0.005692646,0.1320353,0.004370567,0.02652571,0.813126],"study_design_scores_gemma":[0.003851152,0.02775985,0.1833282,0.00215091,0.001477302,0.01335507,0.001932263,0.172645,0.1210298,0.0144612,0.4574432,0.000566094],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5150446,0.002795678,0.4184588,0.001186499,0.0006382167,0.007962285,0.002182353,0.007961437,0.04377016],"genre_scores_gemma":[0.5257632,0.002000961,0.4158958,0.001022755,0.00009006613,0.005551907,0.002492106,0.0003604181,0.0468226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007009106,"threshold_uncertainty_score":0.02344781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05991046294731781,"score_gpt":0.384653526536874,"score_spread":0.3247430635895562,"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."}}