Efficacy of chamomile in pain relief: A systematic review and meta-analysis of clinical trials
Bibliographic record
Abstract
Background: Chamomile is a well-known medicinal herb traditionally used for its analgesic properties. This article aims to provide an updated and critical evaluation of evidence from randomized controlled trials (RCTs) on chamomile’s efficacy for pain relief. Methods: A comprehensive literature search was conducted in Medline, Embase, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials for published RCTs from inception to December 2024. Inclusion criteria comprised RCTs investigating chamomile in any form (oral, inhalation, or topical) compared to placebo or active controls, assessing pain as a primary outcome using validated tools such as the Visual Analog Scale (VAS), Numeric Rating Scale (NRS), or McGill Pain Questionnaire. Standardized mean differences (SMDs) with 95 % confidence intervals (CIs) were calculated using random-effects models. Results A systematic search identified 18 randomized controlled trials (n = 1,525) evaluating chamomile for pain relief. Meta-analysis demonstrated that chamomile was associated with significant pain reduction versus controls (SMD = −0.96; 9 5% CI: −1.36 to −0.57; P < 0.001), with high heterogeneity (I² = 91.1 %). Subgroup analyses showed significant effects in trials that used the Visual Analog Scale (VAS: SMD = −1.12, P < 0.001), with non-significant effects for other pain scales. Chamomile was superior to placebo (SMD = −0.95, P < 0.001) but did not differ significantly from other active treatments (P = 0.074). Conclusion This meta-analysis provides evidence supporting the analgesic efficacy of chamomile. However, substantial heterogeneity across studies suggests variability in design, populations, and protocols, warranting cautious interpretation. Future high-quality, standardized RCTs are needed to clarify effects by formulation, dosing, and clinical context.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.043 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".