Prophylaxis for pediatric postoperative nausea and vomiting: a scoping review of clinical trials
Bibliographic record
Abstract
PURPOSE: Postoperative nausea and vomiting (PONV) is common in pediatric patients undergoing general anesthesia, and clinicians seek prophylactic interventions to prevent its ill effects on patients as well as its ramifications on perioperative care. We sought to assess the body of evidence around prophylactic strategies, both pharmacologic and nonpharmacologic, targeting pediatric PONV. SOURCE: We searched MEDLINE, MEDLINE ePubs Ahead of Print and In-Process Citations, Embase Classic+Embase, the Cochrane Database of Systematic Reviews, Cochrane CENTRAL (via the Ovid platform), Scopus (Elsevier), Web of Science (Clarivate Analytics), ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform, and the International Standard Randomized Controlled Trial Number Registry, from their inception to 23 September 2022. PRINCIPAL FINDINGS: Of 188 clinical trials, 157 (83%) investigated pharmacologic interventions, 25 (13%) investigated nonpharmacologic interventions, and six (3%) investigated mixed pharmacologic and nonpharmacologic interventions. The most common surgeries investigated for pediatric PONV were strabismus surgery (68 trials, 36%) and tonsillectomy or tympanoplasty (45 trials, 23%). Of four measurement tools used to assess PONV in the included trials, the most common was clinical judgement (170 trials, 90%). CONCLUSION: The majority of data in pediatric PONV prophylaxis is based on pharmacologic interventions, with a paucity of research in nonpharmacologic or mixed interventions. Assessing and documenting PONV using tools such as the Baxter Animated Retching Faces Scale or PONV numeric scoring system may help standardize pediatric PONV prophylaxis research moving forward. Furthermore, concurrently assessing pain and adverse effects associated with PONV might further inform our understanding of this complex clinical entity.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".