{"id":"W4414801138","doi":"10.1016/j.jbiomech.2025.113008","title":"Validation of markerless motion capture for spatiotemporal gait measures in people with Parkinson’s disease","year":2025,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Motion capture; Gait; Treadmill; Motion (physics); Intraclass correlation; Gait analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004789984,0.0006810137,0.0005456549,0.001001966,0.0004156035,0.0007955609,0.0007771174,0.0008107635,0.0006691531],"category_scores_gemma":[0.0112021,0.0002759447,0.0006835034,0.0005857144,0.0004773035,0.0004924682,0.001044298,0.0003314581,0.0005510225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997546,"about_ca_system_score_gemma":0.0003934947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001803788,"about_ca_topic_score_gemma":0.003148239,"domain_scores_codex":[0.9952608,0.001768527,0.0007425255,0.000916693,0.001176761,0.0001348837],"domain_scores_gemma":[0.9950368,0.0009502754,0.000807294,0.0007624184,0.002322639,0.0001205343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002657608,0.0005697689,0.7575762,0.0008135735,0.0005665885,0.0004454624,0.003571237,0.002905766,0.07707481,0.0005552128,0.001728847,0.1515351],"study_design_scores_gemma":[0.000141639,0.002114972,0.9587853,0.0002337086,0.000261158,0.001294699,0.0009372652,0.01171945,0.02009577,0.0004066725,0.003941461,0.00006792557],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487255,0.0008611329,0.04684938,0.00009491035,0.0000818347,0.0004429639,0.001176745,0.0001925072,0.001574992],"genre_scores_gemma":[0.9698393,0.000317677,0.02667741,0.0001286658,0.00002941914,0.000608432,0.001811819,0.00004100921,0.000546246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004789984,"threshold_uncertainty_score":0.02533215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02137125364796572,"score_gpt":0.3253626973297493,"score_spread":0.3039914436817836,"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."}}