{"id":"W3157229268","doi":"10.1097/ju.0000000000001832","title":"Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer","year":2021,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; National Cancer Institute","keywords":"Medicine; Biopsy; Prostate cancer; Prostatectomy; Prostate biopsy; Sampling (signal processing); Radiology; Magnetic resonance imaging; Prostate; Cancer detection; Cancer; Urology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003149137,0.00005530784,0.0001814025,0.00002719724,0.00006759231,0.000003925806,0.00004918396,0.00002876689,0.00002249017],"category_scores_gemma":[0.00007866225,0.00002665442,0.00008860248,0.00006356069,0.00005499879,0.00002070429,0.0000171784,0.0001249765,3.063315e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004425134,"about_ca_system_score_gemma":0.0001465604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008556393,"about_ca_topic_score_gemma":0.00006907035,"domain_scores_codex":[0.9994739,0.00004673025,0.0002342965,0.00004986105,0.00008526062,0.0001099427],"domain_scores_gemma":[0.9989872,0.0003583794,0.000259948,0.000102781,0.0002643597,0.0000273094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01014079,0.0004698106,0.01755698,0.0001906948,0.002388831,0.0001068387,0.006320925,0.01609088,0.481692,0.0001094849,0.0002463367,0.4646865],"study_design_scores_gemma":[0.01580171,0.01012309,0.1389958,0.0003657285,0.00706135,0.005576384,0.002386758,0.00605808,0.7664038,0.001879494,0.04508559,0.0002622562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9211255,0.03285672,0.02651747,0.01830241,0.0008122651,0.0003406877,0.00001646096,0.000003965074,0.00002454124],"genre_scores_gemma":[0.992808,0.00595885,0.0005261571,0.0004018765,0.0002573623,0.00001639519,8.369154e-7,0.00000825645,0.00002222855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4644242,"threshold_uncertainty_score":0.1086935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03395341424571971,"score_gpt":0.3149055453714773,"score_spread":0.2809521311257576,"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."}}