The effect of acupuncture on postoperative nausea and vomiting after pediatric tonsillectomy: A meta‐analysis and systematic review
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Tonsillectomy is one of the most frequently performed pediatric surgical procedures worldwide. The complications of this procedure include postoperative nausea and vomiting (PONV) and pain; therefore, both the treatment and prevention of PONV are important. Classical antiemetics include drug therapies such as ondansetron, which are undesirable because they often carry a high cost and several side effects. Therefore, in this study we aimed to evaluate the antiemetic effect of acupuncture after pediatric tonsillectomy. METHODS: We searched for eligible articles that reported on the antiemetic effects of acupuncture after tonsillectomy using the three databases, MEDLINE, Embase, and Cochrane, through July 2015. We included full-length original articles with adequate data for evaluating the antiemetic effects on pediatric tonsillectomy in the form of a relative ratio. The Newcastle-Ottawa scale was used to assess the quality of case control and cohort studies, and the Cochrane risk of bias tool was employed for randomized controlled trials (RCTs). RESULTS: The search identified 415 publications. After screening, we selected eight articles for review (4 RCTs, 3 prospective cohorts, and 1 pilot study). A meta-analysis of acupuncture in pediatric tonsillectomy revealed that the number of patients with PONV was significantly reduced with acupuncture compared to the control group, with a risk ratio of 0.77 (95% confidence interval: 0.63-0.94, P < 0.05). CONCLUSION: When acupuncture at PC6 (neiguan) was used to prevent PONV after pediatric tonsillectomy, the risk ratio was significantly lower compared to that of conventional drug therapy. Although further randomized controlled trials are needed, acupuncture at PC6 is considered an economic and effective treatment for emesis after pediatric tonsillectomy. Laryngoscope, 126:1761-1767, 2016.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".