Trends in Occupational Hearing Loss
Bibliographic record
Abstract
OBJECTIVES: This study aimed to characterize trends in occupational noise-induced hearing loss (ONIHL) incidence and to assess noise exposure levels and changes in audiometric thresholds among workers. METHODS: This retrospective study analyzed audiometric data from 72,952 workers between 1980 and 2019. Incidence rates of ONIHL were calculated. The first and last audiograms for each worker were compared, and noise exposure levels were analyzed. RESULTS: The final cohort included 36,984 workers. ONIHL incidence fluctuated between 4.0% and 7.0%, with a slight upward trend from 2000 to 2019. Noise exposure exceeded 85 dBA for 69.3% of workers with available measurements. Audiometric thresholds significantly worsened from the first to the last audiogram. CONCLUSIONS: Between 1980 and 2019, ONIHL incidence showed a slight increase. Ongoing monitoring of ONIHL trends and enhanced prevention initiatives are critical for mitigating future risks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".