Occupational noise exposure and noise-induced hearing loss are associated with work-related injuries leading to admission to hospital
Bibliographic record
Abstract
OBJECTIVE: This study focuses on work-related injuries that required admission to hospital in a population of male workers exposed to occupational noise (≥80 dBA) which some displayed a hearing loss due to their exposure. METHODS: The study population count 46 550 male workers, 1670 (3.6%) of whom incurred at least one work-related injury requiring admission to hospital within a period of 5 years following hearing tests conducted between 1987 and 2005. The noise exposure and hearing loss-related data were gathered during occupational noise-induced hearing loss (NIHL) screening. The hospital data were used to identify all members of the study population who were admitted, and the reason for admission. Finally, access to the death-related data made it possible to identify participants who died during the course of the study. Cox proportional hazards model taking into account hearing status, noise levels, age and cumulative duration of noise exposure at the time of the hearing test established the risk of work-related injuries leading to admission to hospital. RESULTS: For each dB of hearing loss, a statistically significant risk increase was observed (HR=1.01 dB 95% CI 1.006 to 1.01). An association (HR=2.36 95% CI 2.01 to 2.77) was also found between working in an occupational ambient noise ≥100 dBA and the risk of injury. CONCLUSIONS: From a safety perspective, this issue is highly relevant; especially when workers are exposed to intense ambient noise 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".