Women have worse long-term outcomes after coronary artery bypass grafting than men.
Bibliographic record
Abstract
BACKGROUND: Multiple studies have shown that women have worse in-hospital outcomes than men after coronary artery bypass grafting (CABG). The impact of sex on long-term results following CABG, however, is not as well established. OBJECTIVE: To compare long-term results in men and women undergoing CABG. METHODS: A total of 3404 patients underwent isolated CABG between 1995 and 1999 with follow-up until 2000. Univariate comparisons between men and women were carried out based on pre- and intraoperative variables and short- and long-term adverse outcomes. Long-term adverse outcomes were defined as all-cause mortality or rehospitalization for any cardiac cause, and were risk-adjusted using multivariate modelling techniques. RESULTS: Compared with men, women undergoing CABG were, on average, older (67.8 years versus 64.2 years), more likely to have diabetes (P<0.0001) and hypertension (P<0.0001), and more likely to present for surgery with urgent/emergent status (P<0.0001). Intra-operatively, women had fewer bypasses (3.0 versus 3.3; P<0.0001) and were less likely to receive a left internal mammary artery graft (P=0.0001). While rates of in-hospital mortality were comparable between women and men (2.9% versus 2.2%; P=0.22), women were more likely to experience a long-term adverse event (30.2% versus 23.5%; P<0.0001). After adjusting for clinical differences between men and women, sex emerged as an independent predictor of long-term adverse outcomes following CABG (hazard ratio = 1.18, P=0.03). CONCLUSIONS: Women presented for CABG with more comorbid illness, advanced symptoms and greater urgency than did men. After adjusting for differences in clinical presentation, sex emerged as an independent predictor of long-term adverse outcomes following CABG.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".