Impact of donor age on long-term outcomes after delayed graft function: 10-year follow-up
Bibliographic record
Abstract
Delayed graft function (DGF) has a negative impact on graft survival in donation after brain death (DBD) but not for donation after cardiac death (DCD) kidneys. However, older donor age is associated with graft loss in DCD transplants. We sought to examine the interaction between donor age and DGF in DBD kidneys. This is a single-center, retrospective review of 657 consecutive DBD recipients transplanted between 1990 and 2005. We stratified the cohort by decades of donor age and studied the association between DGF and graft failure using Cox models. The risk of graft loss associated with DGF was not significantly increased for donor age below 60 years (adjusted hazard ratio [aHR] 1.12, 1.51, and 0.90, respectively, for age <40, 41-50 and 51-60 years) but significantly increased after 60 years (aHR 2.67; P = 0.019). Analysis of death-censored graft failure yielded similar results for donor age below 60 years and showed a substantially increased risk with donors above 60 years (aHR 6.98, P = 0.002). This analysis reveals an unexpectedly high impact of older donor age on the association between DGF and renal transplant outcomes. Further research is needed to determine the best use of kidneys from donors above 60 years old, where DGF is expected.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".