Comparing outcomes of third and fourth kidney transplantation in older and younger patients
Bibliographic record
Abstract
Performing third or fourth kidney transplantation (3KT and 4KT) in older patients is rare due to surgical and immunologic challenges. We aimed to analyze and compare the outcomes of younger (18-64 years) and older (≥65 years) recipients of 3KT and 4KT. Between 1990 and 2016, we identified 5816 recipients of 3KTs (153 were older) and 886 recipients of 4KTs (18 were older). The incidences of delayed graft function (24.3% vs. 24.8%, p = .89), primary non-function (3.2% vs. 1.3%, p = .21), 1-year acute rejection (18.6% vs. 14.8%, p = .24), and 5-year death censored graft failure (DCGF) (24.8% vs. 17.9%, p = .06) were not different between younger and older recipients of 3KT. However, 5-year mortality was higher in older recipients (14.0% vs. 33.8%, p < .001) which remained significant after adjustment (aHR = 3.21, 95% CI: 2.59-3.99). Similar patterns were noted in the 4KT cohort. When compared with waitlisted patients, 3KT and 4KT are associated with a lower risk of mortality; aHR = 0.37, 95% CI: 0.33-0.41 and aHR = 0.31, 95% CI: 0.24-0.41, respectively. This survival benefit did not differ by recipient age (younger vs. older, p for interaction = 3KT: .49 and 4KT: .58). In the largest cohort described to date, we report that there is a survival benefit of 3KT and 4KT even among older patients. Although a highly selected cohort, our results support improving access to 3KT and 4KT.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".