Effect of β-Blockers on Perioperative Myocardial Ischemia in Patients Undergoing Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Myocardial ischemia remains a major cause of morbidity in patients undergoing noncardiac surgery. The purpose of the paper was to review the evidence of the use of perioperative beta-blockers for the reduction of myocardial ischemia in patients having noncardiac surgery. METHOD: Pubmed was searched for articles that included beta-blockers and perioperative myocardial ischemia. Randomized controlled trials that assessed the effect of beta-blockers on myocardial ischemia in patients undergoing noncardiac surgery were included in this review and a meta-analysis were performed. RESULTS: Sixteen randomized controlled trials including 2230 patients were included. The study methodologies and results were summarized and meta-analysis performed. Ten trials used beta-blockers in the postoperative period; 954 patients received beta-blockers and 924 patients in the control group. Of the six trials that used beta-blocker for premedication, there were 207 patients in the beta- blocker and 145 patients in the control group. For the cohort when beta-blockers were used postoperatively, myocardial ischemia was reduced significantly with the use of beta-blockers (OR 0.42; 95% CI 0.27-0.65; P=0.0001; I(2)=0%). A similar beneficial effect was observed in trials that used beta- blocker for premedication (OR 0.16; 95% CI 0.07-0.35; P%lt;0.00001; I(2)=40%). CONCLUSION: The meta-analysis shows that the use of beta-blockers, both as premedication and postoperatively, in noncardiac surgery is associated with a significant reduction in perioperative myocardial ischemia.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".