Cortical potentials evoked by tone frequency changes compared to frequency discrimination and speech perception: Thresholds in normal-hearing and hearing-impaired subjects
Bibliographic record
Abstract
Frequency discrimination ability varies within the normal hearing population, partially explained by factors such as musical training and age, and it deteriorates with hearing loss. Frequency discrimination, while essential for several auditory tasks, is not routinely measured in clinical setting. This study investigates cortical auditory evoked potentials in response to frequency changes, known as acoustic change complexes (ACCs), and explores their value as a clinically applicable objective measurement of frequency discrimination. In 12 normal-hearing and 13 age-matched hearing-impaired subjects, ACC thresholds were recorded at 4 base frequencies (0.5, 1, 2, 4 kHz) and compared to psychophysically assessed frequency discrimination thresholds. ACC thresholds had a moderate to strong correlation to psychophysical frequency discrimination thresholds. In addition, ACC thresholds increased with hearing loss and higher ACC thresholds were associated with poorer speech perception in noise. The ACC threshold in response to a frequency change therefore holds promise as an objective clinical measurement in hearing impairment, indicative of frequency discrimination ability and related to speech perception. However, recordings as conducted in the current study are relatively time consuming. The current clinical application would be most relevant in cases where behavioral testing is unreliable.
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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) | 0.001 | 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".