Neighborhood poverty and pediatric allogeneic hematopoietic cell transplantation outcomes: a CIBMTR analysis
Bibliographic record
Abstract
Social determinants of health, including poverty, contribute significantly to health outcomes in the United States; however, their impact on pediatric hematopoietic cell transplantation (HCT) outcomes is poorly understood. We aimed to identify the association between neighborhood poverty and HCT outcomes for pediatric allogeneic HCT recipients in the Center for International Blood and Marrow Transplant Research database. We assembled 2 pediatric cohorts undergoing first allogeneic HCT from 2006 to 2015 at age ≤18 years, including 2053 children with malignant disease and 1696 children with nonmalignant disease. Neighborhood poverty exposure was defined a priori per the US Census definition as living in a high-poverty ZIP code (≥20% of persons below 100% federal poverty level) and used as the primary predictor in all analyses. Our primary outcome was overall survival (OS), defined as the time from HCT until death resulting from any cause. Secondary outcomes included relapse and transplantation-related mortality (TRM) in malignant disease, acute and chronic graft-versus-host disease, and infection in the first 100 days post-HCT. Among children undergoing transplantation for nonmalignant disease, neighborhood poverty was not associated with any HCT outcome. Among children undergoing transplantation for malignant disease, neighborhood poverty conferred an increased risk of TRM but was not associated with inferior OS or any other transplantation outcome. Among children with malignant disease, a key secondary finding was that children with Medicaid insurance experienced inferior OS and increased TRM compared with those with private insurance. These data suggest opportunities for future investigation of the effects of household-level poverty exposure on HCT outcomes in pediatric malignant disease to inform care delivery interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".