Age Disparities in Access to First and Repeat Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that older patients are less frequently placed on the waiting list for kidney transplantation (KT) than their younger counterparts. The trends and magnitude of this age disparity in access to first KT and repeat KT (re-KT) remain unclear. METHODS: Using the US Renal Data System, we identified 2 496 743 adult transplant-naive dialysis patients and 110 338 adult recipients with graft failure between 1995 and 2018. We characterized the secular trends of age disparities and used Cox proportional hazard models to compare the chances of listing and receiving first KT versus re-KT by age (18-64 y versus ≥65 y). RESULTS: Older transplant-naive dialysis patients were less likely to be listed (adjusted hazard ratio [aHR] = 0.18; 95% confidence interval [CI], 0.17-0.18) and receive first KT (aHR = 0.88; 95% CI, 0.87-0.89) compared with their younger counterparts. Additionally, older patients with graft failure had a lower chance of being listed (aHR = 0.40; 95% CI, 0.38-0.41) and receiving re-KT (aHR = 0.76; 95% CI, 0.72-0.81). The magnitude of the age disparity in being listed for first KT was greater than that for re-KT ( Pinteraction < 0.001), and there were no differences in the age disparities in receiving first KT or re-KT ( Pinteraction = 0.13). Between 1995 and 2018, the age disparity in listing for first KT reduced significantly ( P < 0.001), but the age disparities in re-KT remained the same ( P = 0.16). CONCLUSIONS: Age disparities exist in access to both first KT and re-KT; however, some of this disparity is attenuated among older adults with graft failure. As the proportion of older patients with graft failure rises, a better understanding of factors that preclude their candidacy and identification of appropriate older patients are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".