A systematic review and meta-analysis on the use of Hypericum perforatum (St. John's Wort) for pain conditions in dental practice
Bibliographic record
Abstract
BACKGROUND: Hypericum perforatum (St. John's Wort) has been used for a variety of medicinal indications. Most recent research has focussed on its use in herbal form for depression, but its claimed analgesic and anti-inflammatory properties in homeopathic form have also led to a number of studies in patients with acute pain conditions. This systematic review overviews the literature on the use of St. John's Wort for pain conditions in homeopathic dental practice. MATERIAL AND METHODS: PubMed, EMBASE, AMED, CAMbase and the electronic archives of Thieme Publishers were searched with the search terms "(Hypericum OR St. Johns Wort) AND pain". We reviewed and meta-analysed the evidence on Hypericum in pain after tooth extraction was carried out. RESULTS: Twenty one relevant articles were found: four described general recommendations, three basic research, six reported studies in dental care and eight were expert opinions or case reports. Four studies were eligible for the meta-analysis. There was marked high heterogeneity in the effects pain (Chi-Squared = 26.46; I(2) = 0.89). The overall effect of 0.24 (95% CI: [0.06; 1.03]) favours Hypericum but is not statistically significant. CONCLUSION: Although case reports suggest therapeutic potential of Hypericum for pain conditions in dental care, this effect is not currently supported by clinical studies. All studies included in this meta-analysis used Arnica montana as well as Hypericum the results are more influenced by Arnica than Hypericum. Further clinical controlled trials of Hypericum alone in dental practice should be performed.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".