{"id":"W2956168677","doi":"10.1182/blood.2019000239","title":"Combining gene mutation with gene expression analysis improves outcome prediction in acute promyelocytic leukemia","year":2019,"lang":"en","type":"article","venue":"Blood","topic":"Retinoids in leukemia and cellular processes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Cancer Institute; Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; American Society of Hematology","keywords":"Acute promyelocytic leukemia; Myeloid leukemia; Anthracycline; Oncology; Medicine; Retinoic acid; Internal medicine; Mutation; Tretinoin; Daunorubicin; Gene; Leukemia; Malignancy; Cancer research; Bioinformatics; Genetics; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001454853,0.0001684777,0.0002261825,0.0001176364,0.00004668518,0.00003149087,0.0001490059,0.0001674319,0.00001334656],"category_scores_gemma":[0.00001877455,0.0001397281,0.00007797747,0.0003155579,0.00003212696,0.00001259856,0.00006070649,0.0001151463,0.000009562305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004279463,"about_ca_system_score_gemma":0.0001338893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001671719,"about_ca_topic_score_gemma":0.00000844097,"domain_scores_codex":[0.9988355,0.00004457344,0.0002782862,0.0004438206,0.000179045,0.0002187853],"domain_scores_gemma":[0.9994,0.000008060665,0.0001292911,0.0003452769,0.00006660038,0.00005081998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006920051,0.00005992304,0.1985054,0.00003393207,0.0002507053,0.00001435665,0.00006428453,0.000973004,0.7998982,0.000002109504,0.000004914743,0.0001239333],"study_design_scores_gemma":[0.00228481,0.0004153205,0.02408859,0.00001931125,0.0004712724,0.00003275882,0.00006784354,0.0007858581,0.9715942,0.00001332442,0.00004132071,0.0001854418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964452,0.0002888365,0.002506649,0.000007240638,0.00005902594,0.0002714777,0.0000108329,0.0000231933,0.0003875483],"genre_scores_gemma":[0.9957818,0.00004566421,0.002828027,0.00003437167,0.00007441565,0.00005522675,0.0004482953,0.00002350234,0.0007087056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1744168,"threshold_uncertainty_score":0.5697946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004782189312045509,"score_gpt":0.2202746933664957,"score_spread":0.2154925040544502,"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."}}