{"id":"W2116023876","doi":"10.1016/j.gaitpost.2015.10.007","title":"Agreement of spatio-temporal gait parameters between a vertical ground reaction force decomposition algorithm and a motion capture system","year":2015,"lang":"en","type":"article","venue":"Gait & Posture","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; McGill University; Shriners Hospitals for Children - Canada; Polytechnique Montréal; Université du Québec à Montréal; Centre Hospitalier Universitaire Sainte-Justine; Montreal Children's Hospital","funders":"","keywords":"Ground reaction force; Gait; Motion capture; Algorithm; Repeatability; Force platform; Gait analysis; Motion (physics); Motion analysis; Computer science; Simulation; Artificial intelligence; Mathematics; Kinematics; Physical medicine and rehabilitation; Physics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006767208,0.0002133898,0.0003922625,0.0001072442,0.0002370173,0.00001671274,0.00009625483,0.0004313092,0.000003554808],"category_scores_gemma":[0.00004156296,0.0001914482,0.00008400045,0.0001671182,0.0000530585,0.0002725936,0.00006337815,0.0004834506,0.00004080858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003709816,"about_ca_system_score_gemma":0.00009742608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338778,"about_ca_topic_score_gemma":0.0002402311,"domain_scores_codex":[0.997806,0.000472507,0.0006042768,0.0003463992,0.0004070916,0.0003637309],"domain_scores_gemma":[0.9988385,0.0001001665,0.0002936161,0.0002593126,0.0002747465,0.0002336363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003277221,0.002724559,0.7313322,0.008816742,0.001506592,0.0001068062,0.04740687,0.00002921217,0.0574396,0.008005084,0.03767577,0.1016793],"study_design_scores_gemma":[0.003537212,0.0004377312,0.981365,0.0009912762,0.0003168731,0.00002433732,0.007477916,0.002935273,0.00006459919,0.00114329,0.001360654,0.0003458268],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826648,0.000282902,0.01377297,0.0004526622,0.0006667908,0.001134108,0.00009195306,0.0001074086,0.0008264041],"genre_scores_gemma":[0.9951233,0.00001887789,0.003000668,0.0001446945,0.0003360139,0.00007562823,0.001052479,0.0000238441,0.0002245481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2500328,"threshold_uncertainty_score":0.780703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03332358559994598,"score_gpt":0.3329939659053289,"score_spread":0.299670380305383,"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."}}