The effectiveness of different types of acupuncture to reduce symptoms and disability for patients with orofacial pain. A systematic review and meta-analysis
Bibliographic record
Abstract
Purpose To determine the effectiveness of different types of acupuncture in reducing pain, improving maximum mouth opening and jaw functions in adults with orofacial pain.Methods Six databases were searched until 15 June 2023. The Cochrane risk of bias tool and GRADE were employed to evaluate bias and overall evidence certainty.Results Among 52 studies, 86.5% (n = 45) exhibited high risk of bias. Common acupoints, including Hegu LI 4, Jiache ST 6, and Xiaguan ST 7, were used primarily for patients with temporomandibular disorder [TMDs]. Meta-analyses indicated that acupuncture significantly reduced pain intensity in individuals with myogenous TMD (MD = 26.02 mm, I2=89%, p = 0.05), reduced tenderness in the medial pterygoid muscle (standardised mean differences [SMD] = 1.72, I2 = 0%, p < 0.00001) and jaw dysfunction (SMD = 1.62, I2 = 88%, p = 0.010) in mixed TMD when compared to sham/no treatment. However, the overall certainty of the evidence was very low for all outcomes as evaluated by GRADE.Conclusion The overall results in this review should be interpreted with caution as there was a high risk of bias across the majority of randomized controlled trial (RCTs), and the overall certainty of the evidence was very low. Therefore, future studies with high-quality RCTs are warranted evaluating the use of acupuncture in patients with orofacial pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.009 | 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".