Intraoperative Esmolol as an Adjunct for Perioperative Opioid and Postoperative Pain Reduction: A Systematic Review, Meta-analysis, and Meta-regression
Bibliographic record
Abstract
BACKGROUND: Esmolol is an ultrashort β-1 receptor antagonist. Recent studies suggest a role for esmolol in pain response modulation. The authors performed a meta-analysis to determine if the intraoperative use of esmolol reduces opioid consumption or pain scores. METHODS: PubMed, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, pubget, and Google Scholar were searched. Studies were included if they were randomized, placebo- or opioid-controlled trials written in English, and performed on patients 18 years of age or older. For comparison of opioid use, included studies tracked opioid consumption intraoperatively and/or in the postanesthesia care unit. Pain score comparisons were performed during the first hour after surgery. RESULTS: Seventy-three studies were identified, 23 were included in the systematic review, and 19 were eligible for 1 or more comparisons. In 433 patients from 7 trials, intraoperative esmolol decreased intraoperative opioid consumption (Standard Mean Difference [SMD], -1.60; 95% confidence interval [CI], -2.25 to -0.96; P ≤ .001). In 659 patients from 12 trials, intraoperative esmolol decreased postanesthesia care unit opioid consumption (SMD, -1.21; 95% CI, -1.66 to -0.77; P ≤ .001). In 688 patients from 11 trials, there was insufficient evidence of change in postoperative 1 hour pain scores (SMD, -0.60; 95% CI, -1.44 to 0.24; P = .163). CONCLUSIONS: This meta-analysis demonstrates that intraoperative esmolol use reduces both intraoperative and postoperative opioid consumption, with no change in postoperative pain scores.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.050 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".