Haploidentical donor hematopoietic cell transplantation for myelodysplastic/myeloproliferative overlap neoplasms: results from a North American collaboration
Bibliographic record
Abstract
Haploidentical donors offer a potentially readily available donor, especially for non-White patients, for hematopoietic cell transplantation (HCT). In this North American collaboration, we retrospectively analyzed outcomes of first HCT using haploidentical donor and post-transplantation cyclophosphamide (PTCy) in myelodysplastic syndrome/myeloproliferative neoplasm (MDS/MPN) overlap neoplasms (MDS/MPN). We included 120 consecutive patients who underwent HCT using a haploidentical donor for MDS/MPN across 15 centers. Median age was 62.5 years and 38% were of non-White/Caucasian ethnicity. The median follow-up was 2.4 years. Graft failure was reported in seven of 120 (6%) patients. At 3 years, nonrelapse mortality (NRM) was 25% (95% confidence interval [CI]: 17-34), relapse 27% (95% CI: 18-36), grade 3-4 acute graftversus- host disease 12% (95% CI: 6-18), chronic graft-versus-host disease requiring systemic immunosuppression 14% (95% CI: 7-20), progression-free survival (PFS) 48% (95% CI: 39-59), and overall survival (OS) 56% (95% CI: 47-67). On multivariable analysis, NRM was statistically significantly associated with advancing age at HCT (per decade increment, subdistribution hazard ratio [sdHR] =3.28; 95% CI: 1.30-8.25); relapse with the presence of mutation in EZH2/RUNX1/SETBP1 (sdHR=2.61; 95% CI: 1.06-6.44); PFS with advancing age at HCT (per decade increment, HR=1.98, 95% CI: 1.13-3.45); and OS with advancing age at HCT (per decade increment, HR=2.01; 95% CI: 1.11-3.63) and splenomegaly at HCT/prior splenectomy (HR=2.20; 95% CI: 1.04-4.65). Haploidentical donors are a viable option for HCT in MDS/MPN, especially for those disproportionately represented in the unrelated donor registry. Hence, donor mismatch should not preclude HCT for patients with MDS/MPN, an otherwise incurable malignancy. In addition to patient age, disease-related factors including splenomegaly and high-risk mutations dominate outcomes following HCT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".