Differences in Racial and Ethnic Disparities Between First and Repeat Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Recent data suggest patients with graft failure had better access to repeat kidney transplantation (re-KT) than transplant-naive dialysis accessing first KT. This was postulated to be because of better familiarity with the transplant process and healthcare system; whether this advantage is equitably distributed is not known. We compared the magnitude of racial/ethnic disparities in access to re-KT versus first KT. METHODS: Using United States Renal Data System, we identified 104 454 White, Black, and Hispanic patients with a history of graft failure from 1995 to 2018, and 2 357 753 transplant-naive dialysis patients. We used adjusted Cox regression to estimate disparities in access to first and re-KT and whether the magnitude of these disparities differed between first and re-KT using a Wald test. RESULTS: Black patients had inferior access to both waitlisting and receiving first KT and re-KT. However, the racial/ethnic disparities in waitlisting for (adjusted hazard ratio [aHR] = 0.77; 95% confidence interval [CI], 0.74-0.80) and receiving re-KT (aHR = 0.61; 95% CI, 0.58-0.64) was greater than the racial/ethnic disparities in first KT (waitlisting: aHR = 0.91; 95% CI, 0.90-0.93; Pinteraction = 0.001; KT: aHR = 0.68; 95% CI, 0.64-0.72; Pinteraction < 0.001). For Hispanic patients, ethnic disparities in waitlisting for re-KT (aHR = 0.83; 95% CI, 0.79-0.88) were greater than for first KT (aHR = 1.14; 95% CI, 1.11-1.16; Pinteraction < 0.001). However, the disparity in receiving re-KT (aHR = 0.76; 95% CI, 0.72-0.80) was similar to that for first KT (aHR = 0.73; 95% CI, 0.68-0.79; Pinteraction = 0.55). Inferences were similar when restricting the cohorts to the Kidney Allocation System era. CONCLUSIONS: Unlike White patients, Black and Hispanic patients with graft failure do not experience improved access to re-KT. This suggests that structural and systemic barriers likely persist for racialized patients accessing re-KT, and systemic changes are needed to achieve transplant equity.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".