Sex differences in postoperative outcomes for infective endocarditis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Infective endocarditis (IE) remains a prevalent and high-risk condition despite advances in cardiac care. Increasing attention has been directed toward sex-based differences in physiological presentation, disease progression, and surgical management. This review synthesizes evidence on sex-specific differences in IE, with an emphasis on diagnosis, risk factors, disease manifestations, medical management, surgical intervention, and postoperative outcomes. RECENT FINDINGS: While the incidence of IE is more than twice as high in men, women consistently experience worse outcomes. Women present at an older age, with greater comorbidity burden and greater delays in surgical referral. Postoperatively, women are at higher risk of complications - including embolic events, extended ventilation time, and intensive care unit stays - and have significantly higher short-term mortality. Long-term survival is comparable between sexes, suggesting disparities largely influence short-term outcomes. SUMMARY: Awareness of sex-specific differences in risk factors, clinical presentation, intervention bias, complications, and outcomes of IE is essential for optimizing management and equitable care. Further research into sex-based pathophysiology, comorbidity management, and tailored perioperative strategies is critical to advancing patient-centered treatment and optimizing clinical outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".