{"id":"W2590445558","doi":"10.1200/jco.2012.30.30_suppl.40","title":"Development and validation of a digital Gleason score biomarker signature for risk stratification of patients with prostate cancer.","year":2012,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia","funders":"","keywords":"Medicine; Prostate cancer; Prostatectomy; Oncology; Internal medicine; Receiver operating characteristic; Biomarker; Biochemical recurrence; Cutoff; Grading (engineering); Cancer","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":[0.002087217,0.0005362559,0.0007422355,0.001042528,0.0002547704,0.0009503596,0.0007295064,0.0005441928,0.0007263321],"category_scores_gemma":[0.003095146,0.0001836227,0.0005660796,0.0006489427,0.0001878617,0.0003304512,0.00061768,0.0006619989,0.0005295814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008415818,"about_ca_system_score_gemma":0.001244933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005910576,"about_ca_topic_score_gemma":0.007988274,"domain_scores_codex":[0.9994905,0.0001071812,0.00004703174,0.0001424288,0.0001536842,0.00005910658],"domain_scores_gemma":[0.9991608,0.0002719219,0.0001266509,0.00008943495,0.0002424158,0.0001088371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001061587,0.0009051933,0.5234451,0.000173623,0.0004783751,0.0002795359,0.0001232052,0.09487987,0.01161875,0.0003765446,0.007531439,0.3591269],"study_design_scores_gemma":[0.000124842,0.0006738848,0.1234865,0.00003445799,0.0001940348,0.0004852898,0.00007623116,0.8647025,0.006235973,0.001043135,0.002910204,0.00003286685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9624048,0.0009964821,0.02955099,0.0006251751,0.00008914719,0.000323333,0.003378123,0.0007518773,0.001880017],"genre_scores_gemma":[0.9682453,0.0001332896,0.02532629,0.0001136277,0.000025415,0.00008524319,0.005282475,0.00002060314,0.0007677978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005910576,"threshold_uncertainty_score":0.01175237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05944436360528142,"score_gpt":0.4194303748471161,"score_spread":0.3599860112418347,"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."}}