{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01121211,0.00007386315,0.0001297735,0.0001000457,0.0007956098,0.00004328753,0.0002857136,0.00008923432,0.00001406178],"category_scores_gemma":[0.02176816,0.00005184462,0.00001567568,0.0003318766,0.0001228854,0.001602064,0.0001939497,0.0006513011,0.0002093501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001302093,"about_ca_system_score_gemma":0.000158237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00208312,"about_ca_topic_score_gemma":0.0004196398,"domain_scores_codex":[0.995989,0.002566396,0.0003876356,0.0003071706,0.0003372995,0.0004125496],"domain_scores_gemma":[0.9461831,0.05211646,0.0001620637,0.0005712421,0.0008857618,0.00008136196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.01176016,0.003137408,0.1923538,0.03882455,0.0003174218,0.000008758946,0.1324905,0.00001697549,0.005385276,0.1565845,0.2964635,0.162657],"study_design_scores_gemma":[0.002273073,0.0004824632,0.6784164,0.001573112,0.00001062664,0.000002918884,0.2272247,0.03228659,3.77991e-7,0.002460642,0.05514936,0.000119773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8268256,0.00409476,0.00515557,0.1054265,0.001260642,0.0238491,0.0003364127,0.0001782483,0.03287315],"genre_scores_gemma":[0.9906254,0.0003140863,0.006132017,0.0001705644,0.000184505,0.002013316,0.000068769,0.00001228655,0.0004790844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4860625,"threshold_uncertainty_score":0.9864719,"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."}}