Noise exposure and hearing loss in classical orchestra musicians
Bibliographic record
Abstract
Introduction: This study is a follow-up to prior research from our group that attempts to relate noise exposure and hearing thresholds in active performing musicians of the National Ballet of Canada Orchestra. Materials and Methods: Exposures obtained in early 2010 were compared to exposures obtained in early 2017 (the present study). In addition, audiometric thresholds obtained in early 2012 were compared to thresholds obtained in early 2017 (the present study). This collection of measurements presents an opportunity to observe the regularities in the patterns of exposure as well as threshold changes that may be expected in active orchestra musicians over a 5-year span. Results: The pattern of noise exposure across instrument groups, which was consistent over the two time points, reveals highest exposures among brass, percussion/basses, and woodwinds. However, the average noise exposure across groups and time was consistently below 85 dBA, which suggests no occupational hazard. These observations were corroborated by audiometric thresholds, which were generally (a) in the normal range and (b) unchanged in the 5-year period between measurements. Conclusion: Because exposure levels were consistently below 85 dBA and changes in audiometric thresholds were minimal, we conclude that musicians experienced little-to-no risk of noise-induced hearing loss.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".