Heterogeneous cytogenetic subgroups and outcomes in childhood acute megakaryoblastic leukemia: a retrospective international study
Bibliographic record
Abstract
Comprehensive clinical studies of patients with acute megakaryoblastic leukemia (AMKL) are lacking. We performed an international retrospective study on 490 patients (age ≤18 years) with non-Down syndrome de novo AMKL diagnosed from 1989 to 2009. Patients with AMKL (median age 1.53 years) comprised 7.8% of pediatric AML. Five-year event-free (EFS) and overall survival (OS) were 43.7% ± 2.7% and 49.0% ± 2.7%, respectively. Patients diagnosed in 2000 to 2009 were treated with higher cytarabine doses and had better EFS (P = .037) and OS (P = .003) than those diagnosed in 1989 to 1999. Transplantation in first remission did not improve survival. Cytogenetic data were available for 372 (75.9%) patients: hypodiploid (n = 18, 4.8%), normal karyotype (n = 49, 13.2%), pseudodiploid (n = 119, 32.0%), 47 to 50 chromosomes (n = 142, 38.2%), and >50 chromosomes (n = 44, 11.8%). Chromosome gain occurred in 195 of 372 (52.4%) patients: +21 (n = 106, 28.5%), +19 (n = 93, 25.0%), +8 (n = 77, 20.7%). Losses occurred in 65 patients (17.5%): -7 (n = 13, 3.5%). Common structural chromosomal aberrations were t(1;22)(p13;q13) (n = 51, 13.7%) and 11q23 rearrangements (n = 38, 10.2%); t(9;11)(p22;q23) occurred in 21 patients. On the basis of frequency and prognosis, AMKL can be classified to 3 risk groups: good risk-7p abnormalities; poor risk-normal karyotypes, -7, 9p abnormalities including t(9;11)(p22;q23)/MLL-MLLT3, -13/13q-, and -15; and intermediate risk-others including t(1;22)(p13;q13)/OTT-MAL (RBM15-MKL1) and 11q23/MLL except t(9;11). Risk-based innovative therapy is needed to improve patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".