Sex-related differences in systemic inflammatory response and outcomes after cardiac surgery and cardiopulmonary bypass
Bibliographic record
Abstract
OBJECTIVES: Differences in inflammatory responses between men and women may contribute to sex disparities in cardiac surgery outcomes. We investigated how sex differences influence systemic inflammatory response syndrome (SIRS) and adverse outcomes after cardiac surgery. METHODS: A single-centre retrospective cohort study of patients undergoing cardiac surgery from 2018 to 2020 was performed. SIRS was defined as per the American College of Chest Physicians/Society of Critical Care Medicine. Predictors of SIRS and composite adverse outcomes (death, transient ischaemic attack/stroke, renal therapy, bleeding, postcardiotomy mechanical circulatory support, prolonged Intensive Care Unit stay) were evaluated using multivariable logistic regression. Mediation effects of SIRS were assessed using structural equation modelling. RESULTS: The cohort included 1005 patients, of whom 299 (29.8%) were women. SIRS occurred in 28.1% of patients, and 12.7% experienced the composite end point. Female sex was significantly associated with SIRS (odds ratio 1.56; 95% confidence interval 1.12-2.18, P = 0.009) and the composite outcome (odds ratio 1.72; 95% confidence interval 1.10-2.69, P = 0.017). Baseline left ventricular dysfunction and intraoperative hyperlactatemia were additional common predictors. SIRS mediated 50.8% of the effect of female sex, 17.0% of left ventricular dysfunction and 30.9% of intraoperative hyperlactatemia on the composite outcome. CONCLUSIONS: Female sex is independently associated with postoperative SIRS and poorer outcomes. Systemic inflammation, preoperative anaemia and procedural hyperlactatemia are potentially modifiable factors in the mechanisms through which female sex appears to worsen outcome after cardiac surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".