Management of postoperative nausea and vomiting in children: A Systematic Review
Bibliographic record
Abstract
Background: Compared to adults, pediatric surgical patients are more likely to experience postoperative nausea and vomiting (PONV), a common and upsetting complication with more severe outcomes. Its occurrence is caused by a number of factors, such as the type of surgery, anesthesia used, opioid use, and personal risk factors. Despite its widespread occurrence, different clinical settings continue to use different management strategies and diagnostic procedures. The goal is to thoroughly examine and assess the methods currently used to prevent and treat PONV in children having surgery. Objective: The goal is to thoroughly examine and assess the methods currently used to prevent and treat PONV in children having surgery. Methods: Using databases such as PubMed, Scopus, Web of Science, and Google Scholar, a systematic review of peer-reviewed literature was carried out between March and July 2025. Included were studies on pharmacological or non-pharmacological treatments for PONV in children ages 0–18. The study's characteristics, interventions, results, and diagnostic instruments were the main topics of data extraction. The Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool were used to evaluate the quality. Results: A total of 59 studies covering a range of surgical settings and study designs were included, mostly from Asia, North America, and Europe. The prevalence of PONV varied from 18% to 89%, with ENT surgeries having the highest rates. The most prevalent symptoms were nausea and vomiting, which were followed by delayed feeding and drowsiness. Although many studies lacked standardized diagnostic reporting, Rome III/IV criteria were the most commonly reported diagnostic tools. Significant functional effects of PONV included parental work loss, sleep disturbance, and school absence. With differing degrees of success, pharmacologic (such as ondansetron, dexamethasone) and non-pharmacologic (such as acupuncture, ginger) treatments were frequently employed. Conclusion: PONV in children is a common problem with significant social and clinical repercussions. The need for standardized, evidence-based approaches is highlighted by discrepancies in diagnostic criteria and treatment protocols, despite the availability of multiple management strategies. Improving results and patient well-being requires early risk assessment and customized, multimodal preventative measures.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".