Response Pattern Based on the Amplitude of Ear Canal Recorded Cochlear Microphonic Waveforms across Acoustic Frequencies in Normal Hearing Subjects
Bibliographic record
Abstract
Low-frequency otoacoustic emissions (OAEs) are often concealed by acoustic background noise such as those from a patient's breathing and from the environment during recording in clinics. When using electrocochleaography (ECochG or ECoG), such as cochlear microphonics (CMs), acoustic background noise do not contaminate the recordings. Our objective is to study the response pattern of CM waveforms (CMWs) to explore an alternative approach in assessing cochlear functions. In response to a 14-msec tone burst across several acoustic frequencies, CMWs were recorded at the ear canal from ten normal hearing subjects. A relatively long tone burst has a relatively narrow frequency band. The CMW amplitudes among different frequencies were compared. The CMW amplitudes among different frequencies were compared. Two features were observed in the response pattern of CMWs: the amplitude of CMWs decreased with an increase of stimulus frequency of the tone bursts; and such a decrease occurred at a faster rate at lower frequencies than at higher frequencies. Five factors as potential mechanisms for these features are proposed. Clinical applications such as hearing screening are discussed. Therefore, the response pattern of CMWs suggests that they may be used as an alternative to OAEs in the assessment of cochlear functions in the clinic, especially at low frequencies.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".