SEX-RELATED DIFFERENCES AND SYSTEMIC INFLAMMATORY RESPONSE IN CARDIAC SURGERY AND CARDIOPULMONARY BYPASS
Bibliographic record
Abstract
Background: Women experience worse outcome after cardiac surgery, possibly linked to distinct inflammatory patterns. We ascertained how sex affects systemic inflammatory reaction syndrome (SIRS) and outcome in heart surgery patients. Methods: A retrospective evaluation of prospectively collected data from patients who had undergone cardiac surgery from 2018 to 2020 in a single hospital was performed. SIRS was defined as per the American College of Chest Physicians/Society of Critical Care Medicine. Multivariate logistic models were used to identify predictors of SIRS and of a composite outcome including death, transient ischemic attack/stroke, renal replacement therapy, bleeding, postcardiotomy extracorporeal membrane oxygenation or intra-aortic balloon pump, and a ICU stay >96 hours. A structural equation modeling was applied to separate the direct influence of SIRS predictors on the composite outcome from the SIRS-mediated effects. Results: A total of 1,005 consecutive patients were included, 299 (29.8%) were female. SIRS incidence was 28.1%, 128 (12.7%) patients experienced the composite outcome. Female sex was associated with SIRS (OR 1.56; 95% CI 1.12-2.18, p=0.009) and the composite endpoint (OR 1.72; 95% CI 1.10-2.69, p=0.017). Other common predictors were baseline left ventricular impairment and intraoperative hyperlactatemia. SIRS mediated 50.8% of the effect of sex on the outcome, 17.0% of that due to left ventricular dysfunction, and 30.9% of the effect caused by intraoperative hyperlactatemia. Conclusions: Female sex independently predicts postoperative SIRS and poorer outcome. Systemic inflammation, preoperative anemia and procedural hyperlactatemia are key factors in the complex 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 | 0.000 | 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) | 0.002 | 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".