Differences in excess mortality by recipient sex after heart transplant: An individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Identification of differences in mortality risk between female and male heart transplant recipients may prompt sex-specific management strategies. Because worldwide, males of all ages have higher absolute mortality rates than females, we aimed to compare the excess risk of mortality (risk above the general population) in female vs male heart transplant recipients. METHODS: We used relative survival models conducted separately in SRTR and CTS cohorts from 1988-2019, and subsequently combined using 2-stage individual patient data meta-analysis, to compare the excess risk of mortality in female vs male first heart transplant recipients, accounting for the modifying effects of donor sex and recipient current age. RESULTS: We analyzed 108,918 patients. When the donor was male, female recipients 0-12 years (Relative excess risk (RER) 1.13, 95% CI 1.00-1.26), 13-44 years (RER 1.17, 95% CI 1.10-1.25), and ≥45 years (RER 1.14, 95% CI 1.02-1.27) showed higher excess mortality risks than male recipients of the same age. When the donor was female, only female recipients 13-44 years showed higher excess risks of mortality than males (RER 1.09, 95% CI 1.00-1.20), though not significantly (p = 0.05). CONCLUSIONS: In the setting of a male donor, female recipients of all ages had significantly higher excess mortality than males. When the donor was female, female recipients of reproductive age had higher excess risks of mortality than male recipients of the same age, though this was not statistically significant. Further investigation is required to determine the reasons underlying these differences.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.057 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".