Intraoperative Esmolol Infusion in the Absence of Opioids Spares Postoperative Fentanyl in Patients Undergoing Ambulatory Laparoscopic Cholecystectomy
Bibliographic record
Abstract
BACKGROUND: The use of opioids during ambulatory surgery can delay hospital discharge or cause unexpected hospital admission. Preliminary studies using an intraoperative continuous infusion of esmolol in place of an opioid have inconsistently reported a postoperative opioid-sparing effect. In this study, we compared esmolol versus either intermittent fentanyl or continuous remifentanil on postoperative opioid-sparing, side effects, and time of discharge. METHODS: Ninety patients (consisting of three groups) were enrolled in this prospective, randomized, and observer-blinded study. The control group (n = 30) received intermittent doses of fentanyl, the esmolol group (n = 30) received a continuous infusion of esmolol (5-15 microg x kg(-1) x min(-1)) and no supplemental opioids during surgery, and the remifentanil group (n = 30) received a continuous infusion of remifentanil (0.1-0.5 mixrog x kg(-1) x min(-1)). General anesthesia was standardized, and adjuvant medications included acetaminophen, ketorolac, local anesthetics in the skin incisions, dexamethasone, and droperidol. Postoperative analgesia included fentanyl. RESULTS: The amount of fentanyl in the postanesthesia care unit was significantly less in the esmolol group, 91.5 +/- 42.7 microg, compared with the other two groups, remifentanil, 237.8 +/- 54.7 microg, control, 168.1 +/- 96.8 microg (P < 0.0001). The incidence of nausea was more frequent in the control (66.7%) and remifentanil (67.9%) groups compared with the esmolol group (30%) (P < 0.01). The esmolol group reached the White-Song score of 12 of 14 faster than the remifentanil group (P < 0.01), and left the hospital 45-60 min earlier (P < 0.004). CONCLUSIONS: Intraoperative IV infusion of esmolol contributes to a significant decrease in postoperative administration of fentanyl and ondansetron and facilitates earlier discharge.
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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 | 0.000 |
| 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".