The Role of Donor Sex in Females Undergoing Repeat Kidney Transplant: Does Prior Donor Sex Matter?
Bibliographic record
Abstract
Female recipients of male donor kidneys are at increased risk for graft failure because of the HY antigen effect. However, whether prior transplant with a male donor impacts subsequent transplant outcomes is unknown. Therefore, the purpose of this study was to determine whether prior male-current male donor sex is associated with an increased risk of graft failure in female recipients. Methods: We performed a cohort study of adult female recipients undergoing a second kidney transplant (2000-2017), identified using the Scientific Registry of Transplant Recipients. Using multivariable Cox models, we analyzed the risk of death-censored graft loss (DCGL) if the second transplant was from a male versus female kidney donor, conditional on donor sex at the time of the first transplant. In a secondary analysis, we stratified results by recipient age (>50 or ≤50 y) at the time of retransplant. Results: ) posed a higher risk for DCGL in recipients aged >50 y at second transplant (hazard ratio,≤0.67, confidence interval 0.46-0.98, for all other donor combinations), but posed a lower risk if aged ≤50 y at retransplant (hazard ratio, ≥1.37, confidence interval 1.04-1.80, for all other donor combinations). Conclusions: Overall, past-current donor sex pairing was not associated with DCGL in female recipients undergoing second kidney transplant; however, the risk with a past and current female donor was increased in older, and decreased in younger, female recipients at retransplant.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".