Manual therapy and exercise targeted to the neck and orofacial regions for patients with orofacial pain: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To summarize the evidence on the effectiveness of manual therapy (MT) and exercise targeted to the neck or jaw and neck (combined) in the management of orofacial pain (OFP). MATERIAL AND METHODS: The protocol was registered in PROSPERO (CRD42021227490). Electronic searches were conducted in MEDLINE, EMBASE, Cochrane Library, Web of Science, SCOPUS, and CINAHL. Two independent reviewers screened and extracted data. Studies involving adults with OFP treated with MT or exercise targeted to the neck or both the neck and jaw were eligible. Outcomes of interest were pain intensity, maximum mouth opening (MMO), and tenderness (i.e., pain pressure threshold - PPT). The Cochrane risk of bias (RoB) tool and the GRADE approach were used to determine RoB and certainty of the evidence, respectively. RESULTS: Thirty-seven studies were analyzed, mostly with a high RoB. Therapies (i.e., MT alone or combined therapy-MT plus exercise) targeting both the neck and jaw regions improved pain and tenderness (PPT). MT and combined treatment (i.e., MT plus exercise) targeting only the neck were clinically relevant for pain relief. No significant results were found for MMO. CONCLUSIONS: MT isolated and combined therapies targeting the neck alone or neck and jaw are promising for reducing pain and tenderness for individuals with OFP.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.005 | 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".