{"id":"W4295836711","doi":"10.1016/j.annonc.2022.07.1510","title":"1378P Biopsy-based basal-luminal subtyping classifier in high-risk prostate cancer: Analysis of the NRG Oncology/RTOG 9202, 9413, and 9902 randomized phase III trials","year":2022,"lang":"en","type":"article","venue":"Annals of Oncology","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre","funders":"Chugai Pharmaceutical; Genentech; Aptitude Health; European Society for Medical Oncology; Astellas Pharma; Eisai; Incyte; Eli Lilly and Company; AstraZeneca; American Society of Clinical Oncology; Merck KGaA; Gilead Sciences; Ipsen; BeiGene; Daiichi Sankyo Europe; Sanofi; Amgen; Pfizer; Celgene","keywords":"Medicine; Prostate cancer; Internal medicine; Oncology; Cumulative incidence; Subtyping; Biopsy; Basal (medicine); Proportional hazards model; Cancer; Cohort","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":[0.00506025,0.001486885,0.002943174,0.0004197601,0.0003008013,0.001391612,0.0009644759,0.00113601,0.003148627],"category_scores_gemma":[0.004582237,0.0004571257,0.002706455,0.0006587165,0.001126796,0.001540109,0.0006280603,0.003491574,0.00055544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009684428,"about_ca_system_score_gemma":0.00108975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391108,"about_ca_topic_score_gemma":0.003491869,"domain_scores_codex":[0.9977925,0.001565924,0.00008415749,0.0002921801,0.0001515952,0.0001136276],"domain_scores_gemma":[0.9979222,0.00100125,0.0004308752,0.0002140576,0.0001204194,0.0003111675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.8934197,0.002096272,0.01167385,0.001410984,0.008092109,0.0000374737,0.0001054385,0.002172411,0.002314179,0.0003604366,0.003367891,0.07494926],"study_design_scores_gemma":[0.8173851,0.09882537,0.04810406,0.0004386542,0.0195161,0.0002818947,0.0001909193,0.006072306,0.001370916,0.002569666,0.005121657,0.0001232684],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754234,0.01571555,0.001226738,0.001693274,0.0004273118,0.0008316184,0.001816602,0.00009986157,0.002765629],"genre_scores_gemma":[0.989046,0.004138124,0.001483207,0.0009663079,0.0002462067,0.0005070996,0.002317092,0.00004516037,0.001250891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00506025,"threshold_uncertainty_score":0.02676153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1135833553259525,"score_gpt":0.4511089738831809,"score_spread":0.3375256185572285,"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."}}