Total Intravenous Anesthesia Versus Inhalation Anesthesia on Postoperative Analgesia and Nausea and Vomiting After Bariatric Surgery: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Anesthesia for patients with morbid obesity can be challenging because of increased risk of opioid-related adverse events, postoperative nausea and vomiting (PONV), and poor pain control. We conducted a systematic review and meta-analysis to compare the safety and efficacy of total intravenous anesthesia (TIVA) with inhalation anesthesia in patients undergoing bariatric surgery. We searched MEDLINE, EMBASE, CENTRAL, and the Clinical Trials Registry database from inception to July 22, 2020. Primary outcomes were postoperative pain and PONV scores. Secondary outcomes included opioid requirements, intraoperative time, complications, and time to recovery. Grading of Recommendations Assessment, Development, and Evaluation framework was used to rate the certainty of evidence. Among 722 studies identified in our search, 7 randomized studies involving a total of 682 patients met the inclusion criteria. Bariatric surgery with TIVA resulted in a lower incidence of nausea (relative risk [RR], 0.54; 95% CI, 0.31-0.94; P = 0.03; moderate certainty) and vomiting (RR, 0.31; 95% CI, 0.13-0.74; P = 0.008; moderate certainty). There was no difference in postoperative pain at 30 minutes, 1 hour, or 24 hours, or in postoperative opioid requirements. Patients undergoing bariatric surgery with TIVA had significantly lower incidence of PONV but no difference in postoperative pain when TIVA was compared to inhalation anesthesia techniques. These benefits should be considered in order to improve the quality of care and enhance recovery for the bariatric population, who are at an increased baseline risk of perioperative complications. Future adequately powered randomized controlled trials are needed to compare the efficacy of the anesthesia regimens in patients undergoing bariatric surgery.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".