Myeloablative Conditioning for Allogeneic Transplantation Results in Superior Disease-Free Survival for Acute Myelogenous Leukemia and Myelodysplastic Syndromes with Low/Intermediate but not High Disease Risk Index: A Center for International Blood and Marrow Transplant Research Study
Bibliographic record
Abstract
Compared with reduced-intensity conditioning (RIC), myeloablative conditioning (MAC) is generally associated with lower relapse risk after allogeneic hematopoietic cell transplantation (HCT) for acute myelogenous leukemia (AML) and myelodysplastic syndromes (MDS). However, disease-specific risk factors in AML/MDS can further inform when MAC and RIC may yield differential outcomes. We analyzed HCT outcomes stratified by the Disease Risk Index (DRI) in 4387 adults (age 40 to 65 years) to identify the impact of conditioning intensity. In the low/intermediate-risk DRI cohort, RIC was associated with lower nonrelapse mortality (NRM) (hazard ratio [HR], .74; 95% confidence interval [CI], .62 to .88; P < .001) but significantly greater relapse risk (HR, 1.54; 95% CI, 1.35 to 1.76; P < .001) and thus inferior disease-free survival (DFS) (HR, 1.19; 95% CI, 1.07 to 1.33; P = .001). In the high/very high-risk DRI cohort, RIC was associated with marginally lower NRM (HR, .83; 95% CI, .68 to 1.00; P = .051) and significantly higher relapse risk (HR, 1.23; 95% CI, 1.08 to 1.41; P = .002), leading to similar DFS using either RIC or MAC. These data support MAC over RIC as the preferred conditioning intensity for patients with AML/MDS with low/intermediate-risk DRI, but with a similar benefit as RIC in high/very high-risk DRI. Novel MAC regimens with less toxicity could benefit all patients, but more potent antineoplastic approaches are needed for the high/very-high risk DRI group.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".