{"id":"W2000350959","doi":"10.1142/s0219519408002474","title":"HIERARCHICAL ANALYSIS AND CLASSIFICATION OF ASYMPTOMATIC AND KNEE OSTEOARTHRITIS GAIT PATTERNS USING A WAVELET REPRESENTATION OF KINETIC DATA AND THE NEAREST NEIGHBOR CLASSIFIER","year":2008,"lang":"en","type":"article","venue":"Journal of Mechanics in Medicine and Biology","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Notre-Dame","funders":"","keywords":"Osteoarthritis; Gait; Pattern recognition (psychology); Gait analysis; Asymptomatic; Artificial intelligence; Wavelet; k-nearest neighbors algorithm; Computer science; Medicine; Physical medicine and rehabilitation; Pathology","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":[0.00142608,0.0002532882,0.0004535425,0.001380077,0.0002250993,0.0004997649,0.000345638,0.0003691836,0.0005008281],"category_scores_gemma":[0.003945538,0.0001462782,0.0004651809,0.0006858589,0.0003100608,0.0006485117,0.0003163654,0.0003767365,0.0002362122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003108528,"about_ca_system_score_gemma":0.0004095915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002783513,"about_ca_topic_score_gemma":0.001987847,"domain_scores_codex":[0.9992204,0.0001933728,0.00008085791,0.000125326,0.0003076136,0.00007245949],"domain_scores_gemma":[0.9990514,0.0003731452,0.0001223684,0.000107835,0.0003021129,0.00004317082],"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":[0.0005256542,0.0004868013,0.03384832,0.0001980026,0.0001439549,0.0002743274,0.0003561338,0.06613402,0.04170286,0.004447557,0.001325869,0.8505564],"study_design_scores_gemma":[0.00002348637,0.0002218852,0.02804916,0.00002165573,0.00004102507,0.0002572171,0.000115248,0.9620361,0.005392811,0.003144171,0.0006632119,0.00003401033],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3662543,0.0003076905,0.6319282,0.0001090187,0.00004565515,0.0000891442,0.0001440224,0.0002339023,0.0008880796],"genre_scores_gemma":[0.8248002,0.0001255048,0.1739523,0.00002415309,0.00002910144,0.00006495538,0.000324806,0.00001864854,0.0006604866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002783513,"threshold_uncertainty_score":0.007541955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08775159682570491,"score_gpt":0.3414178167054084,"score_spread":0.2536662198797035,"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."}}