Opioid-Associated Postoperative Nausea and Vomiting in Women Undergoing Laparoscopic Hysterectomy: A Network Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: This systematic review and network meta-analysis evaluated the effects of postoperative opioid use on nausea and vomiting in women undergoing laparoscopic hysterectomy. Materials and Methods: A systematic search of PubMed, EMBASE, the Cochrane Library, and RISS was conducted to identify randomized controlled trials that met the eligibility criteria. The Cochrane Risk of Bias 2 tool was used to assess the quality of the included studies. A frequentist network meta-analysis was performed to compare the risks of opioid-associated postoperative nausea and vomiting (O-PONV). Quantitative statistics were presented in forest plots, and the ranking of treatments was determined using the P-score. Results: Seventeen studies involving 1315 participants and 18 postoperative analgesic interventions were included. No significant differences were found among the opioid monotherapies—buprenorphine, butorphanol, fentanyl, oxycodone, sufentanil, and tapentadol. However, among the combination therapies, oxycodone/ketorolac therapy was associated with a significantly higher risk of O-PONV than other ketorolac-containing regimens, including dexmedetomidine, remifentanil, and fentanyl. Conclusions: No significant differences in O-PONV risk were observed among the six opioid monotherapy groups. An opioid-sparing regimen, such as dexmedetomidine/ketorolac, showed a lower risk of O-PONV than an oxycodone-based regimen, underscoring the importance of incorporating patient-centered considerations, such as patient preference and route of administration, into postoperative pain management.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.048 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".