Influence of donor sex and age on graft outcome in kidney transplantation
Bibliographic record
Abstract
BACKGROUND: There is a known recipient sex-dependent association between donor sex and kidney transplant survival. We hypothesized that donor age also modifies the association between donor sex and graft survival. METHODS: First, deceased donor kidney transplant recipients (1988-2019, n = 461 364) recorded in the Scientific Registry of Transplant Recipients, the Australia and New Zealand Dialysis and Transplant Registry and the Collaborative Transplant Study were analyzed. We used multivariable Cox regression models to estimate the association between donor sex and death censored graft loss, accounting for the modifying effects of recipient sex and donor age; donor age was categorized as 5-19, 20-34, 35-49, 50-59 and ≥60 years. Results from cohort-specific Cox models were combined using individual patient data meta-analysis. RESULTS: Among female recipients of donors aged <60 years, graft loss hazards did not differ by donor sex; recipients of female donors ≥60 years showed significantly lower graft loss hazards than recipients of male donors of the same age [combined adjusted hazard ratio (aHR) 0.90, 95% CI 0.86-0.94]. Among male recipients, female donors aged <50 years were associated with significantly higher graft loss hazards than same-aged male donors (5-19 years: aHR 1.11, 95% CI 1.02-1.21; 20-34 years: aHR 1.08, 95% CI 1.02-1.15; 35-49 years: aHR 1.07, 95% CI 1.04-1.10). There were no significant differences in graft loss by donor sex among male recipients of donors aged ≥50 years. CONCLUSION: Donor age modifies the association between donor sex and graft survival. Older female donors were associated with similar or lower hazards of graft failure than older male donors in both male and female recipients, suggesting a better functional reserve of older female donor kidneys.
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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 | 0.005 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".