{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009403641,0.0002610204,0.0006507487,0.001021065,0.00007968569,0.00003145602,0.0003503603,0.0002319751,0.0003567527],"category_scores_gemma":[0.001405074,0.0002196294,0.00005683459,0.001358638,0.0000741963,0.0001177152,0.000379515,0.0004312242,0.0007409066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00276026,"about_ca_system_score_gemma":0.0007038814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006897689,"about_ca_topic_score_gemma":0.0001496663,"domain_scores_codex":[0.9968007,0.0002401584,0.0007288136,0.0007993999,0.0007165812,0.0007143283],"domain_scores_gemma":[0.9976943,0.0008887697,0.0001550446,0.0005745063,0.0002565724,0.0004308122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03075548,0.001115765,0.7086263,0.0002287591,0.000286261,0.0004718734,0.001883118,0.009985133,0.03225264,0.0001362717,0.002086137,0.2121723],"study_design_scores_gemma":[0.002932868,0.006876782,0.971778,0.001024951,0.00007200577,0.0000382796,0.0004584761,0.00788241,0.002223133,0.000313458,0.005871825,0.000527793],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904116,0.00002750283,0.00475346,0.0006541591,0.0004072608,0.002470961,0.00001798761,0.00004223215,0.001214812],"genre_scores_gemma":[0.9617367,0.000005621438,0.03717038,0.0004820572,0.00006935855,0.0000521096,0.00001687915,0.00005604138,0.000410828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2631517,"threshold_uncertainty_score":0.9523102,"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."}}