{"id":"W4394822828","doi":"10.1182/bloodadvances.2023011771","title":"Optimized cytogenetic risk-group stratification of <i>KMT2A</i>-rearranged pediatric acute myeloid leukemia","year":2024,"lang":"en","type":"article","venue":"Blood Advances","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network","funders":"Daiichi Sankyo Europe; National Institutes of Health; Chugai Pharmaceutical; Sumitomo Dainippon Pharma Oncology; National Cancer Institute; Medac; Gilead Sciences; Children's Cancer and Leukaemia Group; Barncancerfonden; Swedish Orphan Biovitrum; Moderna; Syndax Pharmaceuticals; bluebird bio; St. Jude Children's Research Hospital; Jazz Pharmaceuticals; Pfizer; Amgen","keywords":"Myeloid leukemia; Risk stratification; Leukemia; Oncology; Myeloid; Cytogenetics; Medicine; Internal medicine; Biology; Cancer research; Genetics; Gene","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":[0.0004908056,0.000286605,0.0005253298,0.0003716364,0.0001038029,0.00005751535,0.000272644,0.0001612561,0.0001773821],"category_scores_gemma":[0.0001789132,0.0002375519,0.0002168783,0.001006579,0.0001610612,0.0002961372,0.00006845288,0.0005423099,0.0001113797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001822641,"about_ca_system_score_gemma":0.0006871008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006097408,"about_ca_topic_score_gemma":0.00001103621,"domain_scores_codex":[0.9975287,0.0001324678,0.000592717,0.0006235992,0.0006658488,0.0004566331],"domain_scores_gemma":[0.9985221,0.0003203458,0.0001838079,0.0005856517,0.0001715303,0.0002165705],"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":[0.002762395,0.001148521,0.04248841,0.005227858,0.002922074,0.001142657,0.001797857,0.002073572,0.7591614,0.00192303,0.006305287,0.1730469],"study_design_scores_gemma":[0.03659997,0.00736934,0.0805276,0.001534329,0.02068532,0.001623155,0.001934567,0.019644,0.7468145,0.00449819,0.07563035,0.003138657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329374,0.05518416,0.003510752,0.0004748684,0.0004236888,0.00129407,0.0001780628,0.0003941132,0.005602868],"genre_scores_gemma":[0.9313866,0.03488061,0.03109767,0.00005948743,0.000768777,0.0001477025,0.0001554741,0.0000834513,0.001420254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1699083,"threshold_uncertainty_score":0.9687083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01280825383467573,"score_gpt":0.2917258395253536,"score_spread":0.2789175856906779,"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."}}