Donor types and outcomes of transplantation in myelofibrosis: a CIBMTR study
Bibliographic record
Abstract
ABSTRACT: We evaluate the impact of donor types on outcomes of hematopoietic cell transplantation (HCT) in myelofibrosis, using the Center for International Blood and Marrow Transplant Research registry data for HCTs done between 2013 and 2019. In all 1597 patients, the use of haploidentical donors increased from 3% in 2013 to 19% in 2019. In study-eligible 1032 patients who received peripheral blood grafts for chronic-phase myelofibrosis, 38% of recipients of haploidentical HCT were non-White/Caucasian. Matched sibling donor (MSD)-HCTs were associated with superior overall survival (OS) in the first 3 months (haploidentical hazard ratio [HR], 5.80 [95% confidence interval (CI), 2.52-13.35]; matched unrelated (MUD) HR, 4.50 [95% CI, 2.24-9.03]; mismatched unrelated HR, 5.13 [95% CI, 1.44-18.31]; P < .001). This difference in OS aligns with lower graft failure with MSD (haploidentical HR, 6.11 [95% CI, 2.98-12.54]; matched unrelated HR, 2.33 [95% CI, 1.20-4.51]; mismatched unrelated HR, 1.82 [95% CI, 0.58-5.72]). There was no significant difference in OS among haploidentical, MUD, and mismatched unrelated donor HCTs in the first 3 months. Donor type was not associated with differences in OS beyond 3 months after HCT, relapse, disease-free survival, or OS among patients who underwent HCT within 24 months of diagnosis. Patients who experienced graft failure had more advanced disease and commonly used nonmyeloablative conditioning. Although MSD-HCTs were superior, there is no significant difference in HCT outcomes from haploidentical and MUDs. These results establish haploidentical HCT with posttransplantation cyclophosphamide as a viable option in myelofibrosis, especially for ethnic minorities underrepresented in the donor registries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".