Effect of Donor Age and Donor Relatedness on Time to Allogeneic Hematopoietic Cell Transplantation in Acute Leukemia
Bibliographic record
Abstract
Relapse after allogeneic hematopoietic cell transplantation (HCT) for acute leukemia can be reduced when pursued early after first complete remission. The impact of donor age and donor relatedness on the time from diagnosis to transplant in patients with acute leukemia was examined to clarify the design of future prospective studies that can address optimal donor choice. Files of 100 consecutive patients undergoing transplantation for leukemia were reviewed. Recipients of related donors (RDs) and unrelated donors (UDs) were not significantly different in terms of recipient gender, age, underlying diagnosis, disease risk index, graft source, or donor HLA match. UDs were significantly younger than RDs (median age, 29 versus 51, P < .001). Multivariate linear regression revealed that when controlling for age of donor and recipient, the time from diagnosis to transplant was 35% longer with UDs compared with RDs (P = .018). No significant correlation was observed between donor and recipient age on length of time to transplant (P = .134 and P = .850, respectively), when controlling for other variables. The steps in UD procurement that contribute most to the longer time to transplant relate to activating the donor workup and scheduling the donor workup before cell collection. Understanding sources of delay in the transplant process will help transplant centers and UD registries reduce the time to transplant for patients with acute leukemia and will provide necessary insight for the design of prospective controlled studies that can address optimal donor choice.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".