Active Noise Reduction Audiometry: A Prospective Analysis of a New Approach to Noise Management in Audiometric Testing
Bibliographic record
Abstract
OBJECTIVES: To develop a new method of screening audiometry that reduces the adverse effects of low frequency background noise by using active noise reduction (ANR) headphone technology. DESIGN: Prospective testing within an anechoic chamber evaluated the physical properties of ANR headphones. A prospective clinical crossover study compared standard audiometry with ANR headphone audiometry. METHODS: Bose Aviation X circum-aural ANR headphones were tested for both active and passive attenuation properties in a hemi-anechoic chamber using a head and torso simulator. Thirty-seven otology clinic patients then underwent standard audiometry and ANR audiometry, which was performed in a 30- and/or 40-dB sound field. RESULTS: Objective ANR headphone attenuation levels of up to 12 dB were achieved at frequencies below 2,000 Hz. In standard audiometric testing, 40 dB of narrow-band background noise decreased patient pure tone thresholds by 24 dB at 250 Hz. The use of ANR technology provided 12 dB of additional attenuation. This resulted in a significant improvement in test results despite the 40 dB of background noise (P = <0.001). In a 30-dB sound field, standard audiometric thresholds were shifted down by an average of 12 dB. The use of ANR technology completely attenuated this effect and resulted in a significant improvement in results (P = <0.01). These results were identical to those obtained in a quiet sound booth. CONCLUSIONS: Despite a 30-dB sound field, ANR audiometry can produce an audiogram identical to that obtained in a double-walled sound booth. ANR headphone audiometry improves the sensitivity of audiometric screening for mild low-frequency hearing loss. This technology may have important applications for screening in schools, industry, and community practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.025 |
| 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) | 0.000 | 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".