{"id":"W2974725190","doi":"10.1200/po.19.00119","title":"Genomic Biomarkers to Predict Resistance to Hypomethylating Agents in Patients With Myelodysplastic Syndromes Using Artificial Intelligence","year":2019,"lang":"en","type":"article","venue":"JCO Precision Oncology","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Cancer Institute; European Hematology Association","keywords":"Decitabine; Medicine; Cohort; Confidence interval; Myelodysplastic syndromes; Internal medicine; Oncology; Azacitidine; Hypomethylating agent; Bioinformatics; Gene; Genetics; Biology; DNA methylation","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":[0.002214156,0.0007334862,0.000746237,0.002271697,0.0002283882,0.0007627709,0.0004811564,0.0005474135,0.000445891],"category_scores_gemma":[0.005020714,0.0001936267,0.0007300851,0.0009723122,0.0003752078,0.0003351964,0.0004869163,0.0005974535,0.0001169472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004316008,"about_ca_system_score_gemma":0.000629076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650684,"about_ca_topic_score_gemma":0.002000331,"domain_scores_codex":[0.9987565,0.0005934194,0.0001188557,0.0002395289,0.0002229185,0.00006885456],"domain_scores_gemma":[0.9971209,0.00155528,0.0008793683,0.0001152158,0.0002038453,0.0001253751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005544306,0.0002469914,0.9003689,0.00007096792,0.0005703155,0.0002458338,0.00005511911,0.04721451,0.00247619,0.0004025592,0.0004244422,0.04736984],"study_design_scores_gemma":[0.0001579661,0.001228219,0.3156818,0.00005769679,0.0004286445,0.0008199652,0.000101079,0.6726155,0.003494385,0.003937996,0.001435038,0.00004172239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663458,0.001416793,0.02988168,0.0006202006,0.00002088769,0.0000910055,0.0008157038,0.0001217057,0.0006863034],"genre_scores_gemma":[0.9892659,0.0001477647,0.009636709,0.0001038004,0.0000242399,0.00003023199,0.000677158,0.000003373273,0.0001107638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002271697,"threshold_uncertainty_score":0.01170975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05269828464768941,"score_gpt":0.3537519948482408,"score_spread":0.3010537102005514,"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."}}