Herbal extracts in orofacial pain: a systematic review and direct and indirect meta-analysis
Bibliographic record
Abstract
The pharmaceutical industry has been primarily focused on developing synthetic drugs to address orofacial pain (OFP)-related conditions. There is limited knowledge regarding the efficacy of the use of herbal extracts in treating OFP. A systematic review and a meta-analysis of 62 randomized controlled trials assessing the analgesic effects of herbal extracts on pain intensity in various orofacial conditions was conducted. The intervention comprised the use of herbal extracts compared with a placebo and/or standard treatment. The primary outcome was pain intensity assessed before and after the intervention. The pain scores were compared with the baseline scores in each treatment. When compared with standard therapy, the pooled results of the patients who received herbal extracts revealed lower pain intensity in periodontal pain (MD = -0.92[-6.69, 4.85]), oral surgery pain (MD = 18.80[8.80, 28.79]), oral neuropathic pain (MD = 20.34[6.16, 34.52]), endodontic pain (MD = -8.04[-11.72, -4.37]), oral mucosal pain (MD = 8.74[2.76, 14.73]), and temporomandibular pain (MD = 30.94[6.04, 55.83]). The findings indicated a pain-attenuating effect of herbal extracts such as cannabis, turmeric, capsaicin, licorice, ginger, chamomile, clove, Hypericum perforatum, and Arnica montana. These findings revindicate that herbal extracts may be valuable alternatives to traditional pain medications and promising source for the development of new active ingredients for pharmaceuticals.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".