<i>In Situ</i> Otoacoustic Emission Monitoring to Assess the Effects of Noise Exposure on Hearing Health
Bibliographic record
Abstract
Noise-induced hearing loss (NIHL) is the largest cause of action for indemnity in North American industries despite the widespread implementation of hearing conservation programs. Possible causes behind the onset of NIHL are the intervals between hearing tests which are generally too long and the tests are insufficiently sensitive to detect temporary hearing changes to act and prevent permanent hearing damage. Moreover, current noise regulations might be too lenient as to the permissible maximum noise levels. Short-interval hearing assessment could help to observe temporary changes in hearing health and prevent permanent damage. This study investigates the short-term effects of noise exposure characteristics using repeated measurements of otoacoustic emission (OAE) growth functions and presents the most significant predictors of hearing health changes as observed in sixteen individuals equipped with OAE earpieces. The experimental results of this study show that the impulsiveness and frequency spectrum of the noise level could be a possible cause of the decline in OAE levels. As a consequence, hearing conservation programs should consider taking these noise metrics into account for proper NIHL risk assessment. Such noise exposure and hearing health monitoring could greatly improve hearing conservation practices in the workplace by acting faster and eventually mitigate occupational 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".