{"id":"W4384930620","doi":"10.1142/s0219519423400687","title":"KNEE REPLACEMENT RISK PREDICTION MODELING fOR KNEE OSTEOARTHRITIS USING CLINICAL AND MAGNETIC RESONANCE IMAGE FEATURES: DATA FROM THE OSTEOARTHRITIS INITIATIVE","year":2023,"lang":"en","type":"article","venue":"Journal of Mechanics in Medicine and Biology","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Osteoarthritis; Univariate; Nomogram; WOMAC; Lasso (programming language); Proportional hazards model; Medicine; Feature selection; Magnetic resonance imaging; Artificial intelligence; Computer science; Radiology; Machine learning; Surgery; Internal medicine; Multivariate statistics; Pathology","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":[0.002837204,0.0008337644,0.0009569962,0.001372233,0.00023088,0.0009487401,0.0006532696,0.0004866772,0.0007440016],"category_scores_gemma":[0.005883076,0.0001960473,0.001355312,0.001101522,0.0002707415,0.0004846823,0.0007125533,0.0007462615,0.0003073578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000402911,"about_ca_system_score_gemma":0.000672126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006022891,"about_ca_topic_score_gemma":0.006956048,"domain_scores_codex":[0.999146,0.0003432303,0.00008477149,0.0001652301,0.0001860763,0.00007474611],"domain_scores_gemma":[0.9975721,0.001264148,0.0005482038,0.0002257559,0.0002765489,0.000113147],"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":[0.001124206,0.0005919638,0.8343775,0.0003442552,0.0005166436,0.0006157205,0.0001985919,0.05603345,0.00190028,0.0009777733,0.004342519,0.09897705],"study_design_scores_gemma":[0.0001312763,0.0007954499,0.3562052,0.0001962398,0.0006647424,0.00114005,0.0004298993,0.6313299,0.001737341,0.002160006,0.00509324,0.0001166455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9586953,0.002416643,0.03206954,0.0006706881,0.00005281175,0.0001137079,0.00474727,0.0001500068,0.001083978],"genre_scores_gemma":[0.9776937,0.0007545648,0.01297594,0.0001003396,0.00006303079,0.0001133418,0.00790868,0.00001426311,0.0003760641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006022891,"threshold_uncertainty_score":0.01500481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1264883641668743,"score_gpt":0.3762057535415749,"score_spread":0.2497173893747005,"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."}}