Systematic review and meta‐analysis: efficacy of peppermint oil in irritable bowel syndrome
Bibliographic record
Abstract
BACKGROUND: Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction, with a complex pathophysiology. Antispasmodics are prescribed as first-line therapy because of their action on gut dysmotility. In this regard, peppermint oil also has antispasmodic properties. AIM: To update our previous meta-analysis to assess efficacy and safety of peppermint oil, particularly as recent studies have cast doubt on its role in the treatment of IBS METHODS: We searched the medical literature up to 2nd April 2022 to identify randomised controlled trials (RCTs) of peppermint oil in IBS. Efficacy and safety were judged using dichotomous assessments of effect on global IBS symptoms or abdominal pain, and occurrence of any adverse event or of gastro-oesophageal reflux. Data were pooled using a random effects model, with efficacy and safety reported as pooled relative risks (RRs) with 95% confidence intervals (CIs). RESULTS: We identified 10 eligible RCTs (1030 patients). Peppermint oil was more efficacious than placebo for global IBS symptoms (RR of not improving = 0.65; 95% CI 0.43-0.98, number needed to treat [NNT] = 4; 95% CI 2.5-71), and abdominal pain (RR of abdominal pain not improving = 0.76; 95% CI 0.62-0.93, NNT = 7; 95% CI 4-24). Adverse event rates were significantly higher with peppermint oil (RR of any adverse event = 1.57; 95% CI 1.04-2.37). CONCLUSIONS: Peppermint oil was superior to placebo for the treatment of IBS, but adverse events were more frequent, and quality of evidence was very low. Adequately powered RCTs of peppermint oil as first-line treatment for IBS are needed.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".