{"id":"W2294720344","doi":"10.1038/leu.2016.37","title":"Estimating deep molecular responses in chronic myelogenous leukemia: a Bayesian approach","year":2016,"lang":"en","type":"letter","venue":"Leukemia","topic":"Chronic Myeloid Leukemia Treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Chronic myelogenous leukemia; Leukemia; Bayesian probability; Medicine; Oncology; Immunology; Artificial intelligence; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0005825748,0.001513792,0.002140359,0.001058014,0.0001813329,0.00009419853,0.0008433352,0.003137537,0.000432683],"category_scores_gemma":[0.0004914406,0.001270326,0.0006337617,0.0007139443,0.0003747354,0.0001736461,0.0003498027,0.004944562,0.0005899761],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.09023551,"about_ca_system_score_gemma":0.01678989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009238701,"about_ca_topic_score_gemma":0.00006253138,"domain_scores_codex":[0.9922207,0.0004958063,0.001414185,0.002152561,0.001355911,0.002360867],"domain_scores_gemma":[0.9957761,0.0005586084,0.0006205735,0.002549683,0.0001481294,0.0003469377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001506153,0.0002135271,0.001601692,0.009848858,0.002792061,0.1244495,0.001732385,0.003730018,0.01686238,0.0001095899,0.7209573,0.1161965],"study_design_scores_gemma":[0.0483928,0.00271561,0.002192704,0.01271176,0.003310816,0.01433277,0.0001267987,0.09750362,0.005371856,0.001615399,0.8049386,0.006787303],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.2101659,0.04248937,0.07904211,0.4931774,0.005593198,0.02402969,0.0008786694,0.004688588,0.139935],"genre_scores_gemma":[0.1151003,0.0005958576,0.1098655,0.6862206,0.03275783,0.003376144,0.004990701,0.002502413,0.04459069],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1930432,"threshold_uncertainty_score":0.9997611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561515940191343,"score_gpt":0.2642797321289984,"score_spread":0.248664572727085,"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."}}