Outcomes of haploidentical vs matched sibling transplantation for acute myeloid leukemia in first complete remission
Bibliographic record
Abstract
Abstract HLA-haploidentical hematopoietic cell transplantation (Haplo-HCT) using posttransplantation cyclophosphamide (PT-Cy) has improved donor availability. However, a matched sibling donor (MSD) is still considered the optimal donor. Using the Center for International Blood and Marrow Transplant Research database, we compared outcomes after Haplo-HCT vs MSD in patients with acute myeloid leukemia (AML) in first complete remission (CR1). Data from 1205 adult CR1 AML patients (2008-2015) were analyzed. A total of 336 patients underwent PT-Cy–based Haplo-HCT and 869 underwent MSD using calcineurin inhibitor–based graft-versus-host disease (GVHD) prophylaxis. The Haplo-HCT group included more reduced-intensity conditioning (65% vs 30%) and bone marrow grafts (62% vs 7%), consistent with current practice. In multivariable analysis, Haplo-HCT and MSD groups were not different with regard to overall survival (P = .15), leukemia-free survival (P = .50), nonrelapse mortality (P = .16), relapse (P = .90), or grade II-IV acute GVHD (P = .98). However, the Haplo-HCT group had a significantly lower rate of chronic GVHD (hazard ratio, 0.38; 95% confidence interval, 0.30-0.48; P < .001). Results of subgroup analyses by conditioning intensity and graft source suggested that the reduced incidence of chronic GVHD in Haplo-HCT is not limited to a specific graft source or conditioning intensity. Center effect and minimal residual disease–donor type interaction were not predictors of outcome. Our results indicate a lower rate of chronic GVHD after PT-Cy–based Haplo-HCT vs MSD using calcineurin inhibitor–based GVHD prophylaxis, but similar other outcomes, in patients with AML in CR1. Haplo-HCT is a viable alternative to MSD in these patients.
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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 | 0.001 |
| 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".