Race and Socioeconomic Status Influence Outcomes of Unrelated Donor Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Success of hematopoietic cell transplantation (HCT) can vary by race, but the impact of socioeconomic status (SES) is not known. To evaluate the role of race and SES, we studied 6207 unrelated-donor myeloablative (MA) HCT recipients transplanted between 1995 and 2004 for acute or chronic leukemia or myelodysplastic syndrome (MDS). Patients were reported by transplant center to be White (n = 5253), African American (n = 368), Asian/Pacific-Islander (n = 141), or Hispanic (n = 445). Patient income was estimated from residential zip code at time of HCT. Cox regression analysis adjusting for other significant factors showed that African American (but not Asian or Hispanic) recipients had worse overall survival (OS) (relative-risk [RR] 1.47; 95% confidence interval [CI] 1.29-1.68, P < .001) compared to Whites. Treatment-related mortality (TRM) was higher in African Americans (RR 1.56; 95% CI 1.34-1.83, P < .001) and in Hispanics (RR 1.30; 95% CI 1.11-1.51, P = .001). Across all racial groups, patients with median incomes in the lowest quartile (<$34,700) had worse OS (RR 1.15; 95% CI 1.04-1.26, P = .005) and higher risks of TRM (RR 1.21; 1.07-1.36, P = .002). Inferior outcomes among African Americans are not fully explained by transplant-related factors or SES. Potential other mechanisms such as genetic polymorphisms that have an impact on drug metabolism or unmeasured comorbidities, socioeconomic factors, and health behaviors may be important. Low SES, regardless of race, has a negative impact on unrelated donor HCT outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".