{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004355191,0.000335476,0.001094942,0.0003804811,0.000506029,0.001470894,0.0009439277,0.005572276,0.002632925],"category_scores_gemma":[0.0403301,0.0004417659,0.000755668,0.0004845081,0.001061272,0.002091005,0.001046218,0.01273651,0.0008942062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00164314,"about_ca_system_score_gemma":0.001581034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004278922,"about_ca_topic_score_gemma":0.008555297,"domain_scores_codex":[0.9983401,0.001286504,0.00008556006,0.00008600485,0.0001521372,0.0000496853],"domain_scores_gemma":[0.9817555,0.0165062,0.0004407986,0.0003140497,0.0006841385,0.0002992493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009054653,0.0001878716,0.01244409,0.0004838305,0.0003391792,0.001739343,0.0002982487,0.05022049,0.001068446,0.05243896,0.3260539,0.5538202],"study_design_scores_gemma":[0.0008077802,0.0002053752,0.003189458,0.0005704706,0.000219213,0.002683815,0.0002123871,0.2034516,0.001210262,0.6661932,0.121174,0.00008238321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.006796236,0.006913573,0.05596038,0.9229806,0.002323341,0.00004154038,0.0004721049,0.0001297729,0.004382498],"genre_scores_gemma":[0.4486253,0.02693588,0.1117478,0.3555432,0.0434492,0.0004485805,0.0007980795,0.0002570583,0.01219489],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.005572276,"threshold_uncertainty_score":0.02303272,"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."}}