{"id":"W2989851000","doi":"10.1007/978-3-030-43195-2_36","title":"Knee medial and lateral contact forces computed along subject-specific contact point trajectories","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Agence Nationale de la Recherche","keywords":"Contact force; Kinematics; Biomechanics; Contact area; Gait; Knee Joint; Joint (building); Osteoarthritis; Point (geometry); Anatomy; Orthodontics; Mathematics; Computer science; Medicine; Physical medicine and rehabilitation; Geometry; Physics; Engineering; Structural engineering","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":[0.0003980868,0.0004088168,0.0004367578,0.001592232,0.0001647514,0.0006074544,0.0001952382,0.0004789738,0.005114229],"category_scores_gemma":[0.003075355,0.000176986,0.0003255407,0.00117157,0.0001892154,0.0004035448,0.0004540555,0.0002020757,0.0008255954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001952211,"about_ca_system_score_gemma":0.0004868702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00992037,"about_ca_topic_score_gemma":0.01463052,"domain_scores_codex":[0.9997035,0.00004421555,0.00004348225,0.00006783504,0.00009138716,0.00004950249],"domain_scores_gemma":[0.9991428,0.0002320131,0.000152039,0.00006562818,0.0003414238,0.00006610078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005980496,0.0006889435,0.6325206,0.0007595412,0.0006129672,0.001360215,0.002111248,0.0385247,0.04193868,0.001173347,0.005474833,0.2688545],"study_design_scores_gemma":[0.00009933376,0.0007811298,0.9526398,0.00007976562,0.0001482278,0.0008311484,0.001017438,0.03639254,0.005496155,0.0007993152,0.001669297,0.00004593867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881327,0.0001126027,0.005260607,0.00003165099,0.00001666135,0.00005713453,0.004940894,0.0001230823,0.001324726],"genre_scores_gemma":[0.9937534,0.00006238654,0.002147171,0.000005715573,0.000006472524,0.00003641019,0.002950202,0.00001859087,0.00101958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00992037,"threshold_uncertainty_score":0.0197252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305458019515989,"score_gpt":0.2294532883573162,"score_spread":0.2163987081621563,"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."}}