The Older Living Kidney Donor: Part of the Solution to the Organ Shortage
Bibliographic record
Abstract
BACKGROUND: Strategies to increase kidney transplantation are urgently needed. METHODS: We studied all (n = 73,073) first kidney-only transplant recipients in the United States between 1995 and 2003 to determine the incidence and outcomes of living donor transplantation as a function of donor age. Because 90% of living donors were <55 years, we defined older living donors as > or =55 years. Factors associated with transplantation from older living donors and the association of living donor age with allograft function and survival were determined. RESULTS: Recipients of older age, female gender, white race, and preemptive transplants had higher odds of older living donor transplantation. Older living donor transplantation was more likely from spousal donors rather than blood relatives, and more likely when a husband was the donor. The glomerular filtration rate (GFR) one year after transplantation decreased with increasing donor age (P < 0.001). Graft survival from living donors > or =55 years was 85% and 76% at three and five years (compared to 89% and 82% with living donors <55 years, and 82% and 73% with deceased donors <55 years). In a multivariate model, the risk of graft loss with living donors 55-64 years was similar to that with deceased donors <55 years, while recipients from living donors 65-69 years (HR = 1.3, 95% CI: 1.1-1.7) and >70 years (HR = 1.7, 95% CI: 1.1-2.6) had a higher relative risk of graft loss. CONCLUSIONS: Outcomes are excellent with living donors <65 years. Expanded use of older living donors may help meet the demand for transplantation.
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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 | 0.001 | 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) | 0.004 | 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".