Trends in Survival Rates After Allogeneic Hematopoietic Stem-Cell Transplantation for Acute and Chronic Leukemia by Ethnicity in the United States and Canada
Bibliographic record
Abstract
PURPOSE: Differences in survival among ethnic groups in the United States are reported in numerous diseases and treatment strategies. Whether survival after allogeneic hematopoietic stem-cell transplantation (HSCT) differs by ethnicity is uncertain. PATIENTS AND METHODS: Patients (n = 6443) receiving HLA-identical sibling HSCT for acute or chronic leukemia in the United States or Canada between 1985 and 1999 and reported to the International Bone Marrow Transplant Registry were included. The survival of recipients reported as white, black, Hispanic, or Asian was compared using Cox proportional hazards regression adjusting for other clinical factors. Three 5-year periods were studied to evaluate changes over time. RESULTS: Hispanics compared with whites had lower 1-year (53% v 65%; P <.001) and 3-year adjusted survival rates (38% v 53%; P <.001) between 1995 and 1999, the most recent period studied. We failed to find significant differences in survival rates comparing whites with blacks or with Asians in any of the time periods. Overall survival for the entire cohort improved over time, from 56% to 63% at 1 year and from 43% to 51% at 3 years, with greater improvements noted among blacks (45% to 61% at 1 year and 34% to 48% at 3 years). CONCLUSION: Disparities remain in survival rates between whites and Hispanics despite adjustment for clinical factors. Factors not accounted for in this analysis, such as comorbid disease, socioeconomic status, healthcare access and delivery, and psychosocial and cultural variables, require further prospective study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".