Expanding donor options: haploidentical transplant recipients are also highly likely to have a 7/8-matched unrelated donor
Bibliographic record
Abstract
ABSTRACT: The use of haploidentical related donor (HRD) hematopoietic cell transplants (HCTs) in the United States grew by more than fourfold in the last decade, driven mainly by use of posttransplant cyclophosphamide (PTCy)-based graft-versus-host-disease prophylaxis. However, not all patients have a suitable HRD available. In this study, we explored the existence of unrelated donors (URDs) on the National Marrow Donor Program (NMDP) registry at the 8/8- or 7/8-match level for patients receiving HRD HCT in the United States and reporting to the Center for International Blood and Marrow Transplant Research between 2013 and 2020. The data consist of 9696 HRD HCT recipients. The NMDP search prognosis score and a search simulation were used to estimate counts of URD matches on the registry. NMDP search prognosis varied by patient ancestry, with 27.5% non-Hispanic White having a good score compared with 4.6% of African American HRD HCT recipients. Overall, 34% of recipients had ≥1 8/8-matched URDs and 84% had ≥1 7/8 URDs. Recipients of older HRDs (≥35 years) had a likelihood of between 20%- 65% of having ≥5 existing 7/8-matched URDs who were aged ≤35 years. Donor-selection practices varied among the 10 highest-volume HRD centers: 6 had >20% chance of an existing 8/8-matched URD for their HRD recipients, whereas 4 centers had low likelihood of identifying an 8/8-matched URD. In conclusion, although most US patients undergoing HRD HCT do not have an existing 8/8 URD, the majority have an existing 7/8-matched URD. Studies comparing outcomes in patients receiving either HRD or 7/8-matched URD HCT and PTCy-based graft-versus-host disease prophylaxis may be warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.001 |
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 teacher head, 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".