175 The Unfinished Journey towards Transplant Equity: an analysis of racial/ethnic disparities for children in the post-KAS era
Bibliographic record
Abstract
OBJECTIVES/GOALS: Disparities in pediatric kidney transplantation (KT) result in reduced access and worse outcomes for minority children. We aimed to assess the impact of recent systemic changes on these disparities. METHODS/STUDY POPULATION: Retrospective cohort study of pediatric patients utilizing data from the United States Renal Data System (USRDS) and Scientific Registry of Transplant Recipients (SRTR). We compared access to transplantation, time to deceased donor kidney transplant (DDKT), and allograft failure (ACGF) using Cox proportional hazards in the 4 years preceding KAS to the 4 years post-KAS implementation. RESULTS/ANTICIPATED RESULTS: Compared to the pre-KAS era, patients post-KAS were more likely to be pre-emptively listed (26.8% vs 38.1%, p<0.001) and pre-emptively transplanted (23.8% vs 28.0%, p<0.001), however these benefits were not uniform across racial groups. Only 12.7% and 15.7% of Black and Hispanic children received a pre-emptive transplant compared to 29.6%, 49.8% and 54.4% of White, Asian and Other race children respectively. Compared to White children, Black and Hispanic children had a lower likelihood of transplant listing within 2 years of first dialysis service aHR 0.67 (0.59-0.76) and 0.82 (0.73-0.92), in the post-KAS era. Time to DDKT after listing was comparable across all racial groups in both eras. Black children have disproportionally worse 5-yr ACGF, aHR 1.50 (1.08-2.09), p=0.02. DISCUSSION/SIGNIFICANCE: After KAS implementation there remains equity in time to DDKT, however disparities persist in transplant listing and ACGF among Black children. Further studies are needed to identify granular SES factors impacting delayed referral and systemic barriers to transplant, as well as risk factors for poor allograft outcomes among minority children.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".