{"id":"W2987369250","doi":"10.1097/ju.0000000000000518","title":"Reducing Unnecessary Prostate Multiparametric Magnetic Resonance Imaging by Using Clinical Parameters to Predict Negative and Indeterminate Findings","year":2019,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Sunnybrook Health Science Centre; University of Toronto; University Health Network; Health Sciences Centre; Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Medicine; Indeterminate; Magnetic resonance imaging; Prostate; Radiology; Nuclear medicine; Internal medicine","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":[0.001791463,0.0006797415,0.0006179761,0.001189504,0.0002657454,0.0007815821,0.0005076358,0.0007383132,0.0009639689],"category_scores_gemma":[0.007881609,0.0001812428,0.0004238607,0.000354993,0.0002500015,0.0006561561,0.0006230586,0.0007388379,0.0003222156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003014843,"about_ca_system_score_gemma":0.00076481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00115477,"about_ca_topic_score_gemma":0.002115445,"domain_scores_codex":[0.9987152,0.0006530563,0.0001157633,0.0001617569,0.0002530853,0.0001012036],"domain_scores_gemma":[0.9978129,0.0008832954,0.0006514466,0.0001397595,0.0002597828,0.000252756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004310495,0.0005057462,0.9334102,0.0000803429,0.00008697916,0.0001521486,0.00007587629,0.002494099,0.001819846,0.0001053476,0.0008268112,0.06001139],"study_design_scores_gemma":[0.0001422956,0.002674932,0.9378407,0.0001580751,0.0002832079,0.001540429,0.0005190209,0.04940313,0.004645579,0.0008564642,0.001886972,0.00004930124],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831444,0.002368994,0.01087296,0.001674082,0.00009015542,0.0001728657,0.0001680232,0.0001410305,0.00136746],"genre_scores_gemma":[0.9953146,0.0002024617,0.003928897,0.0001795416,0.00005997802,0.00002661704,0.0001328647,0.00000708555,0.0001477852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001791463,"threshold_uncertainty_score":0.009474277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02079058755590413,"score_gpt":0.312106001544413,"score_spread":0.2913154139885089,"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."}}