Trends in the survival benefit of repeat kidney transplantation over the past 3 decades
Bibliographic record
Abstract
Repeat kidney transplantation (re-KT) is the preferred treatment for patients with graft failure. Changing allocation policies, widening the risk profile of recipients, and improving dialysis care may have altered the survival benefit of a re-KT. We characterized trends in re-KT survival benefit over 3 decades and tested whether it differed by age, race/ethnicity, sex, and panel reactive assay (PRA). By using the Scientific Registry of Transplant Recipient data, we identified 25 419 patients who underwent a re-KT from 1990 to 2019 and 25 419 waitlisted counterfactuals from the same year with the same waitlisted time following graft failure. In the adjusted analysis, a re-KT was associated with a lower risk of death (adjusted hazard ratio [aHR] = 0.63; 95% confidence interval [CI], 0.61-0.65). By using the 1990-1994 era as a reference (aHR = 0.77; 95% CI, 0.69-0.85), incremental improvements in the survival benefit were noted (1995-1999: aHR=0.72; 95% CI, 0.67-0.78: 2000-2004: aHR=0.59; 95% CI, 0.55-0.63: 2005-2009: aHR=0.59; 95% CI, 0.56-0.63: 2010-2014: aHR=0.57; 95% CI, 0.53-0.62: 2015-2019: aHR=0.64; 95% CI, 0.57-0.73). The survival benefit of a re-KT was noted in both younger (age = 18-64 years: aHR=0.63; 95% CI, 0.61-0.65) and older patients (age ≥65 years: aHR=0.66; 95% CI, 0.58-0.74; P interaction = .45). Patients of all races/ethnicities demonstrated similar benefits with a re-KT. However, it varied by the sex of the recipient (female patients: aHR=0.60; 95% CI, 0.56-0.63: male patients: aHR=0.66; 95% CI, 0.63-0.68; P interaction = .004) and PRA (0-20: aHR=0.69; 95% CI, 0.65-0.74: 21-80: aHR=0.61; 95% CI, 0.57-0.66; P interaction = .02; >80: aHR=0.57; 95% CI, 0.53-0.61; P interaction < .001). Our findings support the continued practice of a re-KT and efforts to overcome the medical, immunologic, and surgical challenges of a re-KT.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".