Bruxism, Sleep Quality, Anxiety Disorders, and Tension-Type Headache in Temporomandibular Joint Disorders: A Systematic Review
Bibliographic record
Abstract
A BSTRACT Temporomandibular joint disorders (TMD) involve pain and dysfunction of the temporomandibular joint and associated muscles. Emerging evidence suggests associations with bruxism, poor sleep quality, anxiety disorders, and tension-type headache (TTH). To systematically review the associations between bruxism, sleep quality, anxiety disorders, and TTH in patients with TMD. Systematic searches were conducted in PubMed, Scopus, Web of Science, and Cochrane Library up to May 2025. Studies included adults with TMD and reported associations with bruxism, sleep quality, anxiety, or TTH. Risk of bias was assessed using the Newcastle-Ottawa Scale. Data were narratively synthesized. Twenty-six studies were included. Bruxism prevalence ranged from 45% to 87% in TMD patients, with significant associations with myofascial pain ( P < 0.05). Poor sleep quality was reported in 40–75% of TMD patients and correlated with pain severity. Anxiety disorders were present in 30–60% of TMD patients, often exacerbating pain perception. TTH coexisted in 25–65% of TMD patients, sharing central sensitization as a mechanism. Study heterogeneity prevented meta-analysis. Bruxism, poor sleep quality, anxiety disorders, and TTH frequently coexist with TMD. Multidisciplinary assessment is crucial. Future longitudinal studies are warranted.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".