Noise-induced hearing loss and combined noise and vibration exposure
Bibliographic record
Abstract
BACKGROUND: While there is a wide body of literature addressing noise-induced hearing loss (NIHL) and hand-arm vibration syndrome (HAVS) independently, relatively few studies have considered the combined effects of noise and vibration. These studies have suggested an increased risk of NIHL in workers with vibration white finger (VWF), though the relationship remains poorly understood. AIMS: To determine whether hearing impairment is worse in noise-exposed workers with VWF than in workers with similar noise exposures but without VWF. METHODS: The Quebec National Institute of Public Health audiometric database was used in conjunction with work-related accident and occupational diseases data from the Quebec workers' compensation board to analyse differences in audiometry results between vibration-exposed workers in the mining and forestry industries and the overall source population, and between mining and forestry workers with documented VWF and those without VWF. The International Organization for Standardization (ISO) 7029 standards were used to calculate hearing loss not attributable to age. RESULTS: 15751 vibration-exposed workers were identified in an overall source population of 59339. Workers with VWF (n = 96) had significantly worse hearing at every frequency studied (500, 1000, 2000 4000 Hz) compared with other mining and forestry workers without VWF. CONCLUSIONS: This study confirms previous findings of greater hearing loss at higher frequencies in workers with VWF, but also found a significant difference in hearing loss at low frequencies. It therefore supports the association between combined noise and hand-arm vibration (HAV) exposure and NIHL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".