Is playing string or wind musical instruments a risk factor for temporomandibular dysfunction? A Systematic Review.
Bibliographic record
Abstract
Aim. Medical problems specifically affecting professional musicians are commonly mentioned in the literature. The present study is aimed to evaluate, through a systematic review, the possible association between the practice of string with bow and wind musical instruments and the occurrence of Temporomandibular Dysfunction (TMD). Methods. The search for articles was conducted in PubMed/Medline, Web of Science, Scopus, Lilacs, Cochrane Library, and Open Gray databases, and there was no restriction on language or date of publication. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The MeSH terms used were: "music"; "temporomandibular joint"; "temporomandibular joint disorders"; "temporomandibular joint dysfunction syndrome"; and "occupational diseases". Cross-sectional studies, case-control, cohort and clinical trials were included that involved the practice of string with bow and wind musical instruments and the occurrence of Temporomandibular Dysfunction (TMD). Articles were previously selected by title and abstract. Qualitative evaluation was done through the Newcastle-Ottawa Scale. Results. The literature search identified 732 studies, of which 10 met the inclusion criteria, nine of them cross-sectional studies and one a clinical intervention study. The TMD prevalence ranged from 47.0% to 89.0%. Recruitment of participants took place in professional schools and orchestras, and in bands of professional musicians. All studies reported associations between TMD and the practice of musical instruments, and violinists presented higher prevalence rates when compared to other instrument groups. Conclusion. All studies pointed to a possible association between TMD and the practice of string and wind musical instruments. More longitudinal and clinical trials studies are needed to verify any possible interrelationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".