Morbidity and mortality following coronary artery bypass graft surgery in patients with cirrhosis: a population‐based study
Bibliographic record
Abstract
BACKGROUND: The risk of cardiac surgery in patients with cirrhosis is poorly defined. Our objective was to describe outcomes of coronary artery bypass graft (CABG) surgery in cirrhotic patients from a population-based perspective. METHODS: We analysed the 1998-2004 Nationwide In-patient Sample to identify patients hospitalized for CABG surgery. The effect of cirrhosis on mortality, complications, length of stay (LOS) and charges was evaluated using logistic regression models. RESULTS: Between 1998 and 2004, there were 403 094 CABG admissions; 711 patients (0.2%) had cirrhosis. The average annual number of surgeries increased 4.2% [95% confidence interval (CI) 0.7-7.8] in cirrhotic patients, but decreased 5.5% (3.4-7.5) in non-cirrhotic patients. Patients with cirrhosis had an increased risk of mortality [17 vs. 3%; adjusted odds ratio (OR) 6.67; 95% CI 5.31-8.31], complications [43 vs. 28%; OR 1.99 (95% CI 1.72-2.30)] and greater LOS and charges (P<0.0001). Predictors of mortality included age over 60 (OR 2.21; 95% CI 1.31-3.73), female gender (OR 1.92; 95% CI 1.08-3.41), ascites (OR 3.80; 95% CI 1.95-7.39) and congestive heart failure (OR 1.75; 95% CI 1.08-2.84). Hospital volume and off-pump CABG did not affect mortality. CONCLUSIONS: Patients with cirrhosis have an increased risk of morbidity and mortality following CABG surgery. Additional studies are necessary to refine risk stratification in this high-risk patient population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".