Hematopoietic Cell Transplantation Outcomes in Monosomal Karyotype Myeloid Malignancies
Bibliographic record
Abstract
The presence of monosomal karyotype (MK+) in acute myeloid leukemia (AML) is associated with dismal outcomes. We evaluated the impact of MK+ in AML (MK+AML, n = 240) and in myelodysplastic syndrome (MDS) (MK+MDS, n = 221) on hematopoietic cell transplantation outcomes compared with other cytogenetically defined groups (AML, n = 3360; MDS, n = 1373) as reported to the Center for International Blood and Marrow Transplant Research from 1998 to 2011. MK+ AML was associated with higher disease relapse (hazard ratio, 1.98; P < .01), similar transplantation-related mortality (TRM) (hazard ratio, 1.01; P = .90), and worse survival (hazard ratio, 1.67; P < .01) compared with those outcomes for other cytogenetically defined AML. Among patients with MDS, MK+ MDS was associated with higher disease relapse (hazard ratio, 2.39; P < .01), higher TRM (hazard ratio, 1.80; P < .01), and worse survival (HR, 2.02; P < .01). Subset analyses comparing chromosome 7 abnormalities (del7/7q) with or without MK+ demonstrated higher mortality for MK+ disease in for both AML (hazard ratio, 1.72; P < .01) and MDS (hazard ratio, 1.79; P < .01). The strong negative impact of MK+ in myeloid malignancies was observed in all age groups and using either myeloablative or reduced-intensity conditioning regimens. Alternative approaches to mitigate disease relapse in this population are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".