Use of auditory steady-state responses in measuring the occlusion effect of hearing protection devices.
Bibliographic record
Abstract
The most commonly used solution to protect workers from noise exposure consists in using hearing protection devices (HPDs). An important parameter about HPD is the wearing time, since it can decrease the effective protection provided by HPD. However, the recommended wearing time for limiting exposure to noise is not always respected. The occlusion effect (OE) is one of the reasons often given to justify the non-use of HPD: the occlusion of the ear canal induced a modification of the wearer’s voice perception, which creates a discomfort that sometimes brings people to remove their HPD. Present methods of OE measurement have limitations. Objective measurements using microphone do not asses bone conducted sounds directly transmitted to the cochlea and psychophysical measurements at threshold are biased due to the low-frequency masking effect from test-subjects’ physiological noise, in addition to be affected by the variability inherent in subjective measures. We investigated using auditory steady state responses (ASSR) as a technique which might overcome limitations of these other methods. ASSRs were recorded in eight normal hearing adults, using both “normal” and “occluded” conditions. Pure tone stimuli (250 and 500 Hz) were amplitude modulated at 40 Hz and presented through a forehead bone vibrator. “Physiological” OE was calculated as the average difference between the normal and occluded conditions using linear least-square regression of ASSR amplitude data. Physiological OEs were expected to be different from psychophysical OEs, because we used supra-threshold stimulation levels to eliminate the low-frequency masking effect. However, results suggest that the effect of low-frequency masking may not be as large an influence as previously assumed at 250 Hz (87.5% of subjects at 500Hz, and 25% of subjects at 250 Hz had physiological OEs that were greater than psychophysical OEs). Further research, using an extended frequency range, should be done to validate this hypothesis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".