Differences in Liver Graft Survival by Recipient Sex
Bibliographic record
Abstract
We aimed to characterize patterns of differences in heart graft failure rates by recipient sex, accounting for modifying effects of donor sex and recipient age. METHODS: We evaluated 69 246 first heart transplant recipients (1988-2019; Scientific Registry of Transplant Recipients). We used multivariable time-varying Cox models, considering recipient sex by donor sex by recipient age interaction and adjusting for potential confounders. Using the hazard ratio (HR) from the models and a fixed profile of recipient and donor characteristics, we also compared fitted absolute failure rates by recipient sex. RESULTS: Among recipients of male donors, female recipients of all ages had higher failure rates than males (0-12 y: HR 1.36 (95% confidence interval [CI], 1.03-1.81); 13-24 y: 1.43 [1.09-1.88]; 25-44 y: 1.22 [0.95-1.57]; ≥45 y: 1.16 [1.06-1.27]); differences were statistically significant in all age intervals except 25-44 y. When the donor was male, 13 to 24-y-olds showed the largest absolute difference in fitted absolute failure rates, with rates higher by 11.3 failures per 1000 person-y in female than male recipients. Among recipients of female donors, there were no statistically significant differences in graft failure rates between female and male heart recipients of any age. Although point estimates suggested higher failure rates in female than male recipients <25 y (0-12 y: HR 1.19 [95% CI, 0.85-1.66]; 13-24 y: 1.17 [0.84-1.63]), these were not statistically significant. CONCLUSIONS: Female recipients tended to have poorer outcomes than males, particularly at younger ages and when the donor was male, consistent with observations in kidney transplants.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".