Using the Auditory Steady State Response to Record Response Amplitude Curves. A Possible Fast Objective Method for Diagnosing Dead Regions
Bibliographic record
Abstract
In Brief Objectives: To assess a novel objective method of measuring response amplitude curves (RACs) using auditory steady state responses in adults. Design: RACs were recorded in 20 normal-hearing adults. The RACs were measured by recording the changes in the amplitude of the auditory steady state response in the presence of (1) swept frequency narrowband masking noise and (2) fixed narrowband masking noise. Results: The mean recorded RAC tip frequency for a 2-kHz signal was 2250 Hz for the swept masker method and 2239 Hz for the fixed masker method. The estimated repeatability coefficients, calculated using an assumed mean difference of zero, were 389 Hz for the swept method and 342 Hz for the fixed method. Conclusions: These initial results indicate that the swept- and fixed-masking methods appear to be viable and fast ways to record RACs in normal-hearing adults. Further work is needed to further optimize the accuracy of the tip frequency estimation and to establish the normative range of tip frequencies over a wide range of test frequencies in normal-hearing and hearing-impaired subjects. Cochlear dead regions can be diagnosed by recording psychophysical tuning curves. Psychophysical tuning curve recording methods are time consuming and cannot be applied to infants. We propose, using the auditory steady state response, to record response amplitude curves by development of the method that could be used to diagnose and define dead regions in infants while they are asleep.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".