The Effect of Upper Cervical Mobilization/Manipulation on Temporomandibular Joint Pain, Maximal Mouth Opening, and Pressure Pain Thresholds: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
To evaluate the efficacy of upper cervical joint mobilization and/or manipulation on reducing pain and improving maximal mouth opening (MMO) and pressure pain thresholds (PPTs) in adults with temporomandibular joint (TMJ) dysfunction compared with sham or other intervention. MEDLINE, CINAHL, EMBASE, and Cochrane Library from inception to June 3, 2022, were searched. Eight randomized controlled trials with 437 participants evaluating manual therapy (MT) vs sham and MT vs other intervention were included. Two reviewers independently extracted data and assessed risk of bias. Two independent reviewers extracted information about origin, number of study participants, eligibility criteria, type of intervention, and outcome measures. Manual therapy was statistically significant in reducing pain compared with sham (mean difference [MD]: -1.93 points, 95% confidence interval [CI]: -3.61 to -0.24, P=.03), and other intervention (MD: -1.03 points, 95% CI: -1.73 to -0.33, P=.004), improved MMO compared with sham (MD: 2.11 mm, 95% CI: 0.26 to 3.96, P=.03), and other intervention (MD: 2.25 mm, 95% CI: 1.01 to 3.48, P<.001), but not statistically significant in improving PPT of masseter compared with sham (MD: 0.45 kg/cm2, 95% CI: -0.21 to 1.11, P=.18), and other intervention (MD: 0.42 kg/cm2, 95% CI: -0.19 to 1.03, P=.18), or the PPT of temporalis compared with sham (MD: 0.37 kg/cm2, 95% CI: -0.03 to 0.77, P=.07), and other intervention (MD: 0.43 kg/cm2, 95% CI: -0.60 to 1.45, P=.42). There appears to be limited benefit of upper cervical spine MT on TMJ dysfunction, but definitive conclusions cannot be made because of heterogeneity and imprecision of treatment effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".