Abstract 4371043: Sex-Based Differences in Post-Heart Transplant Outcomes: A systematic review and Meta-Analysis
Bibliographic record
Abstract
Background: Sex-based disparities in heart failure and transplant outcomes are increasingly recognized. Although women represent a minority of heart transplant recipients, conflicting data exist on whether recipient sex impacts survival and rejection. We conducted a meta-analysis to assess the impact of recipient sex on post-transplant mortality and rejection outcomes. Methods: We systematically searched PubMed, Embase, and Scopus through February 2025, in accordance with PRISMA guidelines. Eligible observational cohort studies comparing male and female heart transplant recipients and reporting outcomes of mortality and/or rejection were included. Exclusion criteria included pediatric populations, second transplants, and non-original research. 16 studies involving 301,893 patients (228,493 male; 73,400 female) were included. Data extraction was independently performed by three reviewers; risk of bias was assessed using the Newcastle-Ottawa Scale. Statistical analyses were conducted using Review Manager (RevMan 5.4), with hazard ratios (HR) pooled using inverse variance random-effects models. Heterogeneity was evaluated with the I 2 statistic. Results: Meta-analysis of six studies evaluating post-transplant mortality showed no significant difference between male and female recipients (HR: 1.00 [95% CI: 0.88–1.15], p=0.96; I 2 =75%). Seven studies reported on rejection outcomes. Female recipients demonstrated no significant difference in rejection rates compared to males (HR: 0.99 [95% CI: 0.89–1.09], p=0.82; I 2 =53%). Across studies, baseline characteristics including age, comorbidities, and mechanical circulatory support usage were comparable. The overall quality of studies ranged from moderate to high. Conclusion: We found no statistically significant difference in post-transplant mortality or rejection rates between male and female heart transplant recipients. Despite moderate-to-high heterogeneity across studies, the direction and consistency of effect sizes support the conclusion that recipient sex alone does not independently affect major outcomes. These findings reinforce the importance of equitable candidate selection and highlight the need for future prospective studies exploring the biological and sociocultural mechanisms underlying outcome disparities.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".