Classification of audiograms in the prevention of noise-induced hearing loss: A clinical perspective
Bibliographic record
Abstract
BACKGROUND: Noise induced hearing loss (NIHL) is a major contributor to disabling hearing loss. Engineering controls are superior to hearing protection devices (HPDs) in prevention of occupational noise induced hearing loss (ONIHL), although the latter are more commonly used. Effective use of audiometry requires quick categorization of audiograms. The UK Health and Safety Executive (UKHSE) scheme for the categorization of audiograms is a tool that accomplishes this. OBJECTIVES: The objective of this paper is to provide an overview of the classification of audiograms and build a case for the preferential use of the UKHSE's scheme to achieve this. METHOD: The author provides a literature review of methods of classification for audiograms and uses a case study in a Tanzanian mining company to demonstrate how the UKHSE scheme was successfully used to enhance the existing hearing protection program. RESULTS: The literature review identified several methods of classification based on a variation of threshold shifts from baseline. The difference was in the frequency and level of threshold shift used to determine hearing loss, and the recommended course of action once hearing loss is detected. The UKHSE scheme is simple and provides guidance on steps to be taken thereafter. This was demonstrated in a case study among miners in a mining company in Tanzania. CONCLUSION: The UKHSE audiogram classification scheme has the advantage of providing a straightforward, easy to determine classification that allows for intervention appropriate to the findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".