{"id":"W2941261582","doi":"10.1097/01.ju.0000556876.83918.db","title":"PD50-01 ASSESSMENT OF MRI PERFORMANCE IN THE CANARY PROSTATE ACTIVE SURVEILLANCE STUDY (PASS)","year":2019,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Prostate; Medical physics; Radiology; Internal medicine","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":[0.0007202671,0.0003078211,0.0003156647,0.001582343,0.0004310844,0.0009662487,0.0005050828,0.0005512257,0.02780234],"category_scores_gemma":[0.003804391,0.0002436116,0.0003447603,0.001375736,0.0002154924,0.0005551049,0.001014353,0.0005128674,0.006560491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005384686,"about_ca_system_score_gemma":0.0006460121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156999,"about_ca_topic_score_gemma":0.02952144,"domain_scores_codex":[0.9995209,0.00008302106,0.00003978193,0.00006019606,0.000223635,0.00007240791],"domain_scores_gemma":[0.9981455,0.0001903075,0.0004158988,0.0001177369,0.0005865535,0.0005439736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002713704,0.0001907039,0.630564,0.0003117447,0.0002247019,0.0003114086,0.0001079664,0.0002504511,0.001018374,0.0005474627,0.2816965,0.08206303],"study_design_scores_gemma":[0.0002164377,0.0005833518,0.9431294,0.0002931059,0.0001269155,0.0008406629,0.0001423964,0.0003141451,0.0006357626,0.0001926909,0.05348737,0.00003763105],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5623097,0.00821477,0.0008974382,0.007347128,0.001393038,0.0007490508,0.1277802,0.0007114726,0.2905971],"genre_scores_gemma":[0.8433341,0.004002588,0.001099984,0.001401414,0.0007658937,0.0002651878,0.08165373,0.0001868778,0.06729021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02780234,"threshold_uncertainty_score":0.0930081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0079218639893252,"score_gpt":0.3051100528449437,"score_spread":0.2971881888556185,"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."}}