{"id":"W3007977432","doi":"10.1200/jco.2020.38.6_suppl.296","title":"Comparison of micro-ultrasound and multiparametric MRI imaging for prostate cancer: A multicenter prospective analysis.","year":2020,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Ultrasound; Biopsy; Radiology; Magnetic resonance imaging; Prostate cancer; Multiparametric MRI; Prospective cohort study; Prostate; Nuclear medicine; Cancer; Pathology; 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":[0.003634084,0.0004254873,0.0005427093,0.001301969,0.0003242982,0.0008945577,0.0005574628,0.0006200688,0.001512196],"category_scores_gemma":[0.005785319,0.0004665813,0.001000911,0.001677755,0.0003106576,0.0007260566,0.0006657242,0.0004232097,0.0004180079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002353298,"about_ca_system_score_gemma":0.0002132644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008688367,"about_ca_topic_score_gemma":0.001105412,"domain_scores_codex":[0.9971391,0.001258048,0.000248112,0.0006160593,0.0005726805,0.0001660147],"domain_scores_gemma":[0.9932526,0.001558748,0.003432778,0.0006378056,0.0005570799,0.0005609658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004630015,0.00001914794,0.9983715,0.00001945205,0.0001902915,0.00002398885,0.00001744238,0.00002519345,0.0001218854,0.000006453298,0.00003223709,0.0007093324],"study_design_scores_gemma":[0.00002117693,0.0002960616,0.9987771,0.000007000073,0.0001628644,0.0002915567,0.00006351373,0.0001678922,0.00006642548,0.000009046611,0.0001326703,0.000004788189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970366,0.001779171,0.0003020746,0.00003651363,0.00001162318,0.00003183603,0.0004805791,0.000008820621,0.0003128624],"genre_scores_gemma":[0.9992979,0.0001139218,0.0001522172,0.00001595166,0.00001707094,0.00001534425,0.0003403941,0.000003336126,0.00004401333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003634084,"threshold_uncertainty_score":0.0192191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06834707980865913,"score_gpt":0.4994778938279751,"score_spread":0.431130814019316,"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."}}