{"id":"W1994656474","doi":"10.1016/j.gaitpost.2007.11.002","title":"Reliability of a method for analyzing three-dimensional knee kinematics during gait","year":2007,"lang":"en","type":"article","venue":"Gait & Posture","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Kinematics; Gait; Physical medicine and rehabilitation; Observer (physics); Gait analysis; Exoskeleton; Reliability (semiconductor); Computer science; Biomechanics; Knee flexion; Simulation; Medicine; Anatomy; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006521638,0.0001622527,0.0002768918,0.0001064884,0.0000657398,0.00001097901,0.0001075698,0.000150405,0.00001610513],"category_scores_gemma":[0.0002169569,0.0001407636,0.0001584125,0.0002067421,0.00003792024,0.00004952103,0.00003486038,0.0001769295,0.000002948693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005247824,"about_ca_system_score_gemma":0.00001676671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000624255,"about_ca_topic_score_gemma":0.00002635093,"domain_scores_codex":[0.9988849,0.00001209139,0.00045653,0.0001780865,0.0001920479,0.0002763051],"domain_scores_gemma":[0.9989495,0.000371014,0.00007443652,0.0002966495,0.0002288897,0.00007956116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001125526,0.0001560042,0.006281441,0.002179677,0.0001470789,0.000005240934,0.00113755,0.7662184,0.2177246,0.002381514,0.0004724097,0.003183526],"study_design_scores_gemma":[0.002668826,0.0004272624,0.4712189,0.0004962005,0.000290399,0.0000383009,0.0002823138,0.444712,0.05903922,0.01811136,0.001626151,0.001089134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6633749,0.0003635529,0.3350838,0.0001255443,0.0002767057,0.0004346519,0.00003219953,0.0001158808,0.0001927821],"genre_scores_gemma":[0.655522,0.000002962158,0.3442952,0.00002527667,0.00006587955,0.000008553153,0.00001229249,0.00003084852,0.00003706047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4649374,"threshold_uncertainty_score":0.5740175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007250115782480799,"score_gpt":0.2528411686767194,"score_spread":0.2455910528942386,"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."}}