Donor Age and Non-Relapse Mortality: Study of Their Association after HLA-Matched Allogeneic Hematopoietic Cell Transplantation for Acute Myeloid Leukemia and Myelodysplastic Syndrome
Bibliographic record
Abstract
The purpose of this retrospective study was to study the correlation between donor age (DA) and non-relapse mortality (NRM) and relapse incidence (RI) among patients treated with allogeneic hematopoietic cell transplantation (aHCT) for acute myeloid leukemia (AML) or myelodysplastic syndrome (MDS) in a single Canadian center. Data from 125 consecutive patients transplanted with a matched related or unrelated donor between 2015 and 2020 were analyzed using multivariable models. After a median follow-up of 2.8 years, the cumulative incidences of NRM and relapse were 19% and 35% at 5 years. Despite being independently associated with NRM and relapse-free survival (RFS), DA was not associated with RI. The independent determinants of NRM in addition to DA were patient age and hematopoietic cell transplantation comorbidity index (HCT-CI), independently of donor kinship. The effect of DA on NRM was found to be significantly increased over the age of 50 years. DA was not associated with incidence of acute graft-versus-host disease (aGVHD) but showed an association with the occurrence of chronic GVHD (cGVHD). In conclusion, younger donors should be favored to limit NRM and increase RFS in HLA-matched aHCT. The etiological mechanisms behind the association of DA with higher NRM remain to be elucidated.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".