{"id":"W2049392684","doi":"10.1002/jmri.23540","title":"Multiparametric MRI maps for detection and grading of dominant prostate tumors","year":2012,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Medical Research and Materiel Command; Canadian Institutes of Health Research","keywords":"Receiver operating characteristic; Prostate cancer; Grading (engineering); Histopathology; Biopsy; Prostatectomy; Medicine; Diffusion MRI; Cancer detection; Radiology; Prostate; Magnetic resonance imaging; Pattern recognition (psychology); Cancer; Nuclear medicine; Artificial intelligence; Computer science; Pathology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00119314,0.0006624218,0.0003850949,0.002296764,0.0001527048,0.0008005145,0.0004702985,0.0004865556,0.001028859],"category_scores_gemma":[0.003730237,0.0002918822,0.0004074756,0.0005488275,0.0002678771,0.0007245861,0.0004266501,0.0004609246,0.000575577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002910857,"about_ca_system_score_gemma":0.0003591344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005749632,"about_ca_topic_score_gemma":0.0009917511,"domain_scores_codex":[0.9995426,0.0001152847,0.00002475948,0.00008505753,0.000196994,0.00003528727],"domain_scores_gemma":[0.9986726,0.0004327849,0.0002947958,0.0001696003,0.0003382774,0.00009201789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008373653,0.0002888016,0.05071319,0.0004960956,0.0002316673,0.0002600411,0.0001585184,0.01395759,0.3805037,0.001214533,0.001879866,0.5494586],"study_design_scores_gemma":[0.00008546864,0.0009733269,0.2091235,0.0001215721,0.0003853415,0.005026,0.0001337881,0.3377132,0.4342591,0.0038767,0.008125431,0.0001765966],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3867811,0.00333067,0.6036695,0.0003447417,0.00005982967,0.0002162368,0.00068823,0.00258825,0.002321553],"genre_scores_gemma":[0.8028492,0.0006062816,0.1952612,0.00007692957,0.00004808614,0.0001056822,0.0003486709,0.0001307071,0.0005733412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002296764,"threshold_uncertainty_score":0.006309986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256976209634153,"score_gpt":0.2723740519518503,"score_spread":0.2598042898555088,"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."}}