{"id":"W1979870950","doi":"10.1155/2013/846418","title":"Normative Data for the Balance Error Scoring System in Adults","year":2013,"lang":"en","type":"article","venue":"Rehabilitation Research and Practice","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Vancouver Hospital and Health Sciences Centre","funders":"","keywords":"Medicine; Algorithm; Context (archaeology); Population; Machine learning; Normative; Artificial intelligence; Gerontology; Computer science; Environmental health","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":[0.005310509,0.0003731178,0.0003297995,0.003326131,0.0004376941,0.0005742143,0.0007108942,0.0004660923,0.001877094],"category_scores_gemma":[0.02372926,0.0001422698,0.000320429,0.001773124,0.0002330203,0.0005126272,0.0005407311,0.0005409417,0.000979703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005756376,"about_ca_system_score_gemma":0.0007402612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006255919,"about_ca_topic_score_gemma":0.008487069,"domain_scores_codex":[0.9963841,0.000575269,0.0008806959,0.0003030087,0.001749311,0.0001074801],"domain_scores_gemma":[0.9874768,0.002417408,0.00164695,0.0007225115,0.007346135,0.0003902999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003937052,0.000315092,0.8919638,0.0001510202,0.0000684836,0.000138458,0.0006499462,0.0005936166,0.00098917,0.0008205737,0.01551543,0.08840057],"study_design_scores_gemma":[0.00004326356,0.0004068239,0.9851415,0.0001393452,0.00003051872,0.0009357316,0.0004733727,0.001185938,0.0006430696,0.0005275999,0.01044681,0.00002600059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183462,0.002186282,0.0195754,0.0003872326,0.0003378712,0.001297498,0.03279465,0.0007198679,0.02435507],"genre_scores_gemma":[0.9173159,0.0007394661,0.02173754,0.0002169623,0.00009444815,0.003252992,0.05440797,0.0001234374,0.002111285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006255919,"threshold_uncertainty_score":0.02808499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1499974242441518,"score_gpt":0.5107120154632988,"score_spread":0.3607145912191471,"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."}}