Allocation of renal grafts to older recipients does not result in loss of functioning graft-years.
Bibliographic record
Abstract
BACKGROUND: Most deceased donor kidney allocation protocols are based on waiting time and do not take into account either recipient's life expectancy. This study investigates whether graft survival is affected by patient life expectancy. METHODS: A total of 640 adult kidney transplants were performed. Recipients were divided in group A (patients ≤ 50 years) and group B (patients > 50 years). The status of graft+recipient combination was characterized as: a) deceased recipient with functional graft, b) alive recipient with functional graft and c) deceased or alive recipient with nonfunctional graft. RESULTS: Mean kidney recipient survival was 15.15 (95% CI: 14.54, 15.77) and 12.40 (95% CI: 11.47, 13.33) years for groups A and B respectively (p < 0.0001). Mean graft survival was 13.62 (95% CI: 12.81, 14.43) and 12.42 (95% CI: 11.59, 13.25) years for groups A and B respectively (p=0.6516). Non-functional grafts were identified in 18.4% (n=57) and 16.4% (n=54) of group A and B respectively. CONCLUSIONS: Allocation of renal grafts to older patients does not result in significant loss of graft-years. Recipients' life expectancy has a small impact on graft survival. We should not deviate from the basic principles of equality, when kidney allocation systems are designed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".