Prevalence of musculoskeletal disorders in symphonic orchestra’s musicians: A cross-sectional study in Italy
Bibliographic record
Abstract
Introduction: This study aims to investigate the incidence and characteristics of musculoskeletal disorders among professional musicians, exploring the correlations between the use of different musical instruments and the onset of such pathologies. Methods: In this cross-sectional study design, we utilized a convenience sample of musicians from an orchestra under health surveillance. The Italian version of the Nordic Musculoskeletal Questionnaire was employed, and data were examined through descriptive statistics. Results: About 50% of the musicians in our study exhibit musculoskeletal symptoms during musical performances, with a higher prevalence among viola and violin players. Musculoskeletal pathologies were found among cellists, double bass players, and brass and woodwind musicians. A high incidence of these disorders was also observed among percussionists. The most affected age group is around forty years, with a particular emphasis on symptoms in the neck and dorsal spine. Discussion and Conclusions: This research’s implications highlight the need for targeted prevention and training programs for musicians, underscoring the importance of preventive interventions and a multidisciplinary approach to reducing the incidence of these pathologies. The focus on training and prevention in music schools and conservatories emerges as crucial for a proactive approach to managing musicians’ health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".