Sex Differences in Excess Mortality Among Waitlisted Kidney, Heart, and Liver Transplant Candidates
Bibliographic record
Abstract
Background: Sex differences in excess mortality risk (ie, above expected in the age-, sex-, and race-matched general population) among candidates waitlisted for transplant may reflect sex bias in access to the waitlist, disparities in allocation policies, and/or sex differences in care for patients with organ failure. Methods: We used time-dependent relative survival models to determine the relative excess risk of mortality in females compared with males recorded in the Scientific Registry of Transplant Recipients who were waitlisted for kidney, heart, or liver transplant from 1988 to 2019, accounting for the modifying effects of candidate age and listing era. Results: Among 644 262 kidney and 106 353 heart candidates, excess mortality was higher in female than male kidney candidates <60 y, but lower in female kidney candidates ≥60 y and heart candidates ≥12 y; patterns did not differ by era. Among 259 230 liver candidates, patterns differed by era of waitlisting. Excess mortality was lower for female than male liver candidates 0-12 and 25-44 y, and higher for females than males 13-24 y, without differences by era. Excess mortality was lower for female than male liver candidates 45-59 y waitlisted 1988-2011, but not different by sex for those waitlisted 2012-2019. Among liver candidates ≥60 y, excess mortality did not differ by sex for those waitlisted 1988-2011 but was higher for females than males waitlisted 2012-2019. Conclusions: The patterns of sex differences in excess mortality observed among waitlisted transplant candidates likely reflect the selection of healthier, lower-risk females than males for waitlisting and higher mortality risks for females with organ failure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".