CABG and Preoperative use of Beta-Blockers in Patients with Stable Angina are Associated with Better Cardiovascular Survival
Bibliographic record
Abstract
OBJECTIVE: In contrast to unstable angina, optimal therapy in patients with stable angina is debated. Our aim was to evaluate the outcomes of patients with stable angina scheduled for isolated coronary artery bypass grafts and the effect of preoperative use of beta-blockers. Overall and cardiovascular survivals were our primary outcome. Operative mortality and postoperative complications along with subgroup analysis of diabetic patients were our secondary outcomes. METHODS: Retrospective evaluation of patients with stable angina scheduled for isolated coronary artery bypass grafts was included. Pre- and postoperative variables were extracted from the institution database. Survival was obtained from the National Registry. RESULTS: We included 282 patients with stable angina, with a mean age of 65.6±9.5 years. 26.6% were female and 38.7% had diabetes. Three-vessel disease was present in 76.6% of patients. Previous beta-blocker treatment was evident in 69.9% of patients. 10-year overall survival in the whole population was 60.5% (95% confidence interval [CI]: 50.3-70.7%). Operative mortality during the study period was 3.5%. Patients with preoperative use of beta-blocker therapy had better overall survival (9.0 years, 95%CI: 8.6-9.5) than those without treatment (7.9 years, 95%CI: 7.1-8.8 years; P=0.048). Predictors for overall survival were: hypertension, diabetes, and age. Predictors for cardiovascular survival in diabetic patients were: beta-blocker use, gender, and age. CONCLUSION: Coronary artery bypass grafts surgery in patients with stable angina carries low operative mortality, postoperative complications, and excellent long-term cardiovascular survival. The preoperative use of beta-blockers in diabetic patients is associated with better cardiovascular survival after coronary artery bypass grafts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".