Evidence-based review on temporomandibular disorders among musicians
Bibliographic record
Abstract
BACKGROUND: Playing a musical instrument that loads the masticatory system has frequently been linked to temporomandibular disorders (TMDs). Previous literature reviews on this topic do not conform to the current standards of evidence-based medicine. AIMS: To investigate the effects of playing a musical instrument (i.e. violin/viola and wind instruments) or singing on the presence of TMDs, based on evidence derived from observational studies. METHODS: Databases of Medline, Web of Science and Google Scholar were searched using MeSH and other relevant terms. For each study, a quality assessment was undertaken using a modified version of the Newcastle-Ottawa Scale (NOS). RESULTS: Fifteen relevant papers were identified for inclusion in this review. Of the seven possible points that could be scored with the NOS, the majority of these studies scored under half. Based on the available evidence, the purported relationship between the playing of specific musical instruments and TMDs was not as evident as reported in previous literature reviews. CONCLUSIONS: There is limited evidence to conclude that playing a wind instrument is a hazard to the temporomandibular system. Furthermore, there is no available evidence to suggest that vocalists experience more TMDs than controls. The studies that investigated the presence of TMDs among violists and violinists yielded ambiguous outcomes; some studies reported no association between the playing of these instruments and the presence of signs and symptoms of TMDs, whereas in studies where a clinical examination was performed (though of lower methodological quality), an association was found.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".