Contralateral Ear Occlusion for Improving the Reliability of Otoacoustic Emission Screening Tests
Bibliographic record
Abstract
Newborn hearing screening is an established healthcare standard in many countries and testing is feasible using otoacoustic emission (OAE) recording. It is well documented that OAEs can be suppressed by acoustic stimulation of the ear contralateral to the test ear. In clinical otoacoustic emission testing carried out in a sound attenuating booth, ambient noise levels are low such that the efferent system is not activated. However in newborn hearing screening, OAEs are often recorded in hospital or clinic environments, where ambient noise levels can be 60-70 dB SPL. Thus, results in the test ear can be influenced by ambient noise stimulating the opposite ear. Surprisingly, in hearing screening protocols there are no recommendations for avoiding contralateral suppression, that is, protecting the opposite ear from noise by blocking the ear canal. In the present study we have compared transient evoked and distortion product OAEs measured with and without contralateral ear plugging, in environmental settings with ambient noise levels <25 dB SPL, 45 dB SPL, and 55 dB SPL. We found out that without contralateral ear occlusion, ambient noise levels above 55 dB SPL can significantly attenuate OAE signals. We strongly suggest contralateral ear occlusion in OAE based hearing screening in noisy environments.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".