Living donor age and kidney transplant outcomes: an assessment of risk across the age continuum
Bibliographic record
Abstract
Detailed data on living donor age, and its interplay with recipient age, in predicting allograft and recipient outcomes are wanting. We used the Scientific Registry of Transplant Recipients (2000-2009, n = 49 589) to assess the effect of living donor age on delayed graft function (DGF), total graft failure, death-censored graft failure, death with graft function, and graft failure with death as a competing risk using logistic and Cox proportional hazards models. Potential nonlinear associations were modeled using fractional polynomial functions. There was a significant 1.87-fold increase in the adjusted odds of DGF in the oldest versus youngest age groups. The 10-year adjusted hazard ratios (HR) for total graft failure, death-censored graft failure, and death with graft function increased in a nonlinear fashion across the range of living donor age studied. Graft failure was most accentuated in the youngest recipient age groups in competing risk models. Adjustment for renal function at 6- and 12-months post-transplant markedly attenuated the association between living donor age and graft/patient outcomes. Our findings confirm the important influence of living donor age on transplant outcomes and provide detailed estimates of risk across the living donor age continuum.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".