Exploring Outcomes by Ethnicity in Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Background: Clinical outcome disparities among racial and ethnic groups have been described following allogeneic hematopoietic cell transplantation (HCT). This study investigated the impact of race and ethnicity on HCT outcomes in a multi-ethnic single-center population. Methods: We analyzed outcomes of 709 allogeneic HCT patients, stratified by racial and ethnic groups, who underwent allogeneic HCT between January 2018 and April 2022. Outcomes examined included overall survival (OS), cumulative incidence of relapse (CIR), non-relapse mortality (NRM), and graft-versus-host disease/relapse-free survival (GRFS). Results: No significant differences in OS, CIR, NRM, GRFS, acute GVHD (aGVHD), or chronic GVHD (cGVHD) were observed. Significant differences in age, use of human leukocyte antigen-mismatched donors (HLA-MM), and HCT-CI comorbidity scores ≥ 3 across racial and ethnic groups were observed. Overall mean age was 58 years, with Black patients having the youngest mean age of 43 (range 22–73) and White patients the highest mean age of 59 (range 18–76) (p < 0.001). HCT-CI score ≥ 3 was seen in 35.9% of the entire cohort, varying by race and ethnicity: 60.5% in Black, 41.4% in South Asian, 31.5% in White, and 29.0% in East Asian patients (p < 0.001). Utilization of HLA-MM donors (including haploidentical) was 29.2% overall, with highest frequencies in Black (65.1%) and East Asian (45%) patients, and lowest in White patients (20.4%) (p < 0.001). Conclusions: Statistically significant differences were observed across self-identified racial and ethnic groups regarding age, HCT-CI ≥ 3, and the use of HLA-MM donors. However, post-allogeneic HCT outcomes did not differ significantly by race or ethnicity. Larger prospective trials are warranted to validate our findings.
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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 | 0.003 |
| 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 | 0.001 |
| 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".