{"id":"W2990787575","doi":"10.3390/diagnostics9040219","title":"A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer","year":2019,"lang":"en","type":"article","venue":"Diagnostics","topic":"Cancer, Lipids, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prostate cancer; Medicine; Prostate; Cohort; Disease; Oncology; Feature selection; Grading (engineering); Cancer; Artificial intelligence; Machine learning; Internal medicine; Computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223549,0.0001859339,0.000203492,0.00007933212,0.00003847055,0.00003002076,0.0001593767,0.00009781785,0.0000315406],"category_scores_gemma":[0.0001565291,0.0001778343,0.00007450797,0.0001617881,0.00004328203,0.000005729821,0.0001307741,0.0002001994,0.00003233155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003699556,"about_ca_system_score_gemma":0.0001119899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002473324,"about_ca_topic_score_gemma":0.0004382375,"domain_scores_codex":[0.9987828,0.00004909264,0.0002013758,0.0004345412,0.0001624521,0.0003697805],"domain_scores_gemma":[0.999462,0.00004170101,0.00004720237,0.0002690892,0.00003974489,0.0001402257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001222456,0.0004678144,0.3794697,0.0001310323,0.0001861795,0.000004058955,0.001495076,0.122296,0.3425026,0.001158603,0.1211151,0.02995145],"study_design_scores_gemma":[0.00150931,0.0001854097,0.01553144,0.0000804731,0.00001909786,0.000003464073,0.00006977739,0.007488859,0.006644033,0.00012479,0.9679031,0.0004402384],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862947,0.00845654,0.0007412599,0.0007060873,0.002706585,0.0005281981,0.0001693218,0.0000163244,0.000380938],"genre_scores_gemma":[0.973721,0.02009186,0.0007841807,0.0009278219,0.002264322,0.0001427571,0.0002631392,0.00006180082,0.001743135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.846788,"threshold_uncertainty_score":0.7251872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110716034245409,"score_gpt":0.258108087133157,"score_spread":0.2470009267907029,"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."}}