Age-dependent Sex Differences in Graft Loss After Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Sex differences in kidney graft loss rates were reported in the United States. Whether these differences are present in other countries is unknown. METHODS: We estimated the association between recipient sex and death-censored graft loss in patients of all ages recorded in the Scientific Registry of Transplant Recipients, Australia and New Zealand Dialysis and Transplant Registry, and Collaborative Transplant Study registries who received a first deceased donor kidney transplant (1988-2019). We used multivariable Cox regression models, accounting for the modifying effects of donor sex and recipient age, in each registry separately; results were combined using individual patient data meta-analysis. RESULTS: We analyzed 438 585 patients. Young female patients 13-24 y old had the highest crude graft loss rates (female donor: 5.66; male donor: 5.50 per 100 person-years). Among young recipients of male donors, females showed higher graft loss risks than males (0-12 y: adjusted hazard ratio [aHR] 1.42, (95% confidence interval [CI], 1.17-1.73); 13-24 y: 1.24 (1.17-1.32); 25-44 y: 1.09 (1.06-1.13)). When the donor was female, there were no significant differences by recipient sex among those of age <45 y; however, the aHR for females was 0.93 (0.89-0.98) in 45-59 y-old and 0.89 (0.86-0.93) in ≥ 60 y-old recipients. Findings were similar for all 3 registries in most age intervals; statistically significant heterogeneity was seen only among 13-24-y-old recipients of a female donor (I2 = 71.5%, P = 0.03). CONCLUSIONS: There is an association between recipient sex and kidney transplantation survival that is modified by recipient age and donor sex.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".