The Effectiveness of Noninvasive Interventions for Temporomandibular Disorders
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness and cost-effectiveness of noninvasive interventions for temporomandibular disorders (TMD). METHODS: We systematically searched MEDLINE, EMBASE, CINAHL, PsycINFO, and Cochrane Central register from 1990 to 2014 for effectiveness studies and the Cochrane Health Technology Assessment Database, EconLit, NHS Economic Evaluation Database, and Tufts Medical Center Cost-Effectiveness Analysis Register from 1990 to 2014 for cost-effectiveness studies. Random pairs of independent reviewers critically appraised eligible studies using the Scottish Intercollegiate Guidelines Network criteria. Evidence from eligible studies was synthesized using best-evidence synthesis methodology. RESULTS: Our search for effectiveness studies yielded 16,995 citations; 31 were relevant and 7 randomized controlled trials (published in 8 articles) had a low risk of bias. We found no relevant cost-effectiveness studies. The evidence suggests that for persistent TMD: (1) cognitive-behavioral therapy and self-care management lead to similar improvements in pain and disability but cognitive-behavioral therapy is more effective for activity interference and depressive symptoms; (2) cognitive-behavioral therapy combined with usual treatment provides short-term benefits in pain and ability to control pain compared with usual treatment alone; (3) intraoral myofascial therapy may reduce pain and improve jaw opening; and (4) structured self-care management may be more effective than usual treatment. The evidence suggests that occlusal devices may not be effective in reducing pain and improving motion for TMD of variable duration. Evidence on the effectiveness of biofeedback is inconclusive. DISCUSSION: The available evidence suggests that cognitive-behavioral therapy, intraoral myofascial therapy, and self-care management are therapeutic options for persistent TMD.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".