{"id":"W2950209709","doi":"10.5489/cuaj.5526","title":"Prediction of prostate cancer by deep learning with multilayer artificial neural network","year":2018,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prostate cancer; Receiver operating characteristic; Logistic regression; Artificial neural network; Stepwise regression; Prostate; Medicine; Prostate biopsy; Artificial intelligence; Regression analysis; Cancer; Statistics; Computer science; Machine learning; Mathematics; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002831432,0.0001163753,0.0002282857,0.00005903302,0.0002787926,0.0000386375,0.00004239394,0.0001237792,0.0004528965],"category_scores_gemma":[0.0001141561,0.00008205167,0.00006188062,0.0001930778,0.00006094312,0.00007870195,0.000006230886,0.0003795259,0.00000884512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008431799,"about_ca_system_score_gemma":0.0002692317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001246021,"about_ca_topic_score_gemma":0.002240587,"domain_scores_codex":[0.9987509,0.00009308914,0.0002843235,0.0001657268,0.0002712262,0.0004346925],"domain_scores_gemma":[0.998728,0.00005281108,0.0003057757,0.00005618162,0.0004224242,0.0004347685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00021842,0.00004678668,0.9749136,0.000003202111,0.0001503216,0.00002316988,0.000199454,0.001144546,0.0001317539,0.00001191302,0.008047376,0.01510948],"study_design_scores_gemma":[0.001487515,0.00293718,0.9412504,0.00004713469,0.0002018321,0.00005838559,0.000078829,0.003816162,0.0004245053,0.0001031799,0.04948081,0.0001140954],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898458,0.0007481101,0.00004521811,0.008346114,0.0003273766,0.0002519696,0.00006948089,0.00002138112,0.0003445831],"genre_scores_gemma":[0.9971389,0.0002507926,0.0001057565,0.001259024,0.0009341216,0.00002994204,0.00003607684,0.000013066,0.0002323779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04143343,"threshold_uncertainty_score":0.4958899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661976989774585,"score_gpt":0.2373323954480599,"score_spread":0.220712625550314,"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."}}