{"id":"W2522030021","doi":"","title":"CAN KNEE MOTION PATTERNS BE USED AS A PREDICTIVE TOOL FOR IMPLANT STATUS AND LONGEVITY","year":2018,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Kinematics; Match moving; BitTorrent tracker; Medicine; Motion (physics); Motion analysis; Tracking (education); Gait; Computer science; Physical medicine and rehabilitation; Simulation; Artificial intelligence; Psychology; Eye tracking","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":[0.001600952,0.0007227052,0.0005751002,0.002640653,0.0001580074,0.001248973,0.0004436543,0.0008692444,0.002266616],"category_scores_gemma":[0.008857546,0.0002565435,0.0006367593,0.001373166,0.0003825192,0.0007731104,0.0003919161,0.0003892339,0.000863986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001587904,"about_ca_system_score_gemma":0.0002022035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070774,"about_ca_topic_score_gemma":0.0019427,"domain_scores_codex":[0.9994055,0.0001546642,0.0001036721,0.0001360733,0.0001372209,0.00006290476],"domain_scores_gemma":[0.9945945,0.00182976,0.002537258,0.0002643882,0.0005397826,0.000234252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002577106,0.00006247069,0.9639437,0.000134895,0.0000747498,0.00005913726,0.00006811637,0.0005602748,0.001234934,0.00003488988,0.0003106394,0.03325855],"study_design_scores_gemma":[0.0000110804,0.0006706882,0.9921773,0.0001179968,0.00009728094,0.000603197,0.0002021263,0.004194919,0.0007363762,0.0002251846,0.000938937,0.00002498665],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851326,0.004019281,0.006057937,0.0003948018,0.0001037778,0.00008394249,0.001840279,0.0001519814,0.002215312],"genre_scores_gemma":[0.9944149,0.0009637091,0.00330437,0.00007573489,0.00008969398,0.00006097443,0.0006724942,0.00001130131,0.0004067379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002640653,"threshold_uncertainty_score":0.008466721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03402407321674807,"score_gpt":0.2603744826978747,"score_spread":0.2263504094811266,"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."}}