An Investigation of the Adjustment of Retrospective Noise Exposure for Use of Hearing Protection Devices
Bibliographic record
Abstract
OBJECTIVE: To account for use of hearing protection devices (HPDs) in retrospective noise exposure assessment, adjust noise exposure estimates accordingly, and validate the adjusted estimates. BACKGROUND: A previous study in the same working population showed a stronger relation for noise and acute myocardial infarction among those who did not wear HPD. Because accurate noise exposure assessment is complicated by the use of HPD, we previously developed a multilevel model of the likelihood of HPD use for British Columbia (Canada) lumber mill workers. Historical estimates of noise exposure can be adjusted according to models predictions and a reduction in misclassifying workers, exposure is expected. METHODS: Work history and exposure information were obtained for 13,147 lumber mill workers followed from 1909 until 1998. Audiometric data for the cohort, including hearing threshold levels at several pure tone frequencies, were obtained from the local regulatory agency for the period from 1978 to 2003. Following the modeling of HPD use, noise estimates were adjusted according to models predictions and attenuation factors based on existing research and standards. Adjusted and unadjusted noise metrics were compared by investigating their ability to predict noise-induced hearing loss. RESULTS: We showed a 4-fold increase in the noise exposure and hearing loss slope, after adjusting for HPD use, while controlling for gender, age, race, as well as medical and non-occupational confounding variables. CONCLUSION: While the relative difference before and after adjustment for use of HPD is considerable, we observed a subtle absolute magnitude of the effect. Using noise-induced hearing loss as a 'gold standard' for testing the assessment of retrospective noise exposure estimates should continue to be investigated.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".