Matched unrelated vs haploidentical donor hematopoietic cell transplantation using posttransplant cyclophosphamide
Bibliographic record
Abstract
ABSTRACT: Posttransplant cyclophosphamide (PTCy)-based graft-versus-host disease (GVHD) prophylaxis is now standard for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT). Previous studies comparing MUD and haploidentical donor HCT using PTCy were limited in size and follow-up. We therefore performed a registry-based analysis examining the impact of donor type on HCT with PTCy. Adult patients (n = 5873) receiving MUD (n = 1973) or haploidentical (n = 3900) HCT with PTCy for acute leukemia (74.2%) or myelodysplastic syndrome (MDS; 25.8%) reported to the Center for International Blood and Marrow Transplant Research between 2017 and 2021 were included. Primary end points were 3-year overall survival (OS) and GVHD-free, relapse-free survival (GRFS). Cox regression and sensitivity analyses were performed through adjustment of propensity scores. Haploidentical HCT had worse OS (hazard ratio [HR], 1.15; 95% confidence interval [CI], 1.04-1.27; P = .005) and GRFS (HR, 1.19; 95% CI, 1.10-1.29; P < .001) versus MUD HCT. Donor age was the only other donor factor associated with survival. Results were confirmed in sensitivity analysis. When restricted to reduced intensity conditioning or donors <30 years, OS did not differ between groups. Haploidentical HCT was associated with higher primary graft failure (HR, 1.67; P = .002), increased grade 3/4 acute GVHD (HR, 1.28; P = .039), higher moderate/severe chronic GVHD (HR, 1.47; P < .001), and nonrelapse mortality (HR, 1.34; P < .001). Grade 2 to 4 acute GVHD and relapse risk did not differ. This large analysis showed that in adults with acute leukemia or MDS, MUD HCT was associated with improved outcomes versus haploidentical HCT with PTCy-based GVHD prophylaxis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".