{"id":"W2064459320","doi":"10.1111/j.1464-410x.2007.07219.x","title":"Patient selection determines the prostate cancer yield of dynamic contrast‐enhanced magnetic resonance imaging‐guided transrectal biopsies in a closed 3‐Tesla scanner","year":2007,"lang":"en","type":"article","venue":"British Journal of Urology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Prostate cancer; Medicine; Prostate; Magnetic resonance imaging; Biopsy; Transrectal ultrasonography; Radiology; Cancer; Intraepithelial neoplasia; Prostate biopsy; Prostate-specific antigen; Urology; 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":[0.0009043885,0.0002078272,0.0003062077,0.0003483421,0.0001649817,0.0004198524,0.0001770224,0.0005050905,0.000740069],"category_scores_gemma":[0.005472811,0.0001716723,0.000245659,0.0002284692,0.0002542972,0.0003831921,0.0002298731,0.0002123566,0.0002545743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001697265,"about_ca_system_score_gemma":0.000178981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002891383,"about_ca_topic_score_gemma":0.0007776368,"domain_scores_codex":[0.9994624,0.0002435444,0.00005341732,0.00008610957,0.0001128045,0.00004170242],"domain_scores_gemma":[0.9973841,0.00154446,0.0004782405,0.00008041031,0.0002016155,0.0003111558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001368719,0.00006974638,0.9756325,0.00002795886,0.00004805297,0.0006668664,0.0001069154,0.0002842699,0.008368241,0.00001434456,0.0001235739,0.0132888],"study_design_scores_gemma":[0.00007629163,0.00164664,0.9808185,0.00002134146,0.0001008752,0.01028874,0.0001723565,0.002181052,0.004325153,0.00005180709,0.0002984447,0.00001882297],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989931,0.0003782285,0.0003308694,0.00004048054,0.000003252149,0.000003820751,0.00001746654,0.0000105098,0.0002223913],"genre_scores_gemma":[0.9995074,0.00007969017,0.0003187543,0.00002463204,0.00000780091,0.000002043809,0.00003456722,0.000003004242,0.00002220694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009043885,"threshold_uncertainty_score":0.004782856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006545235345763656,"score_gpt":0.2532804823679061,"score_spread":0.2467352470221424,"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."}}