Coding-in-Noise Deficits are Not Seen in Responses to Amplitude Modulation in Subjects with cochlear Synaptopathy Induced by a Single Noise Exposure
Bibliographic record
Abstract
Since the first report of noise-induced synaptic damage in animals without permanent threshold shifts (PTSs), the concept of noise-induced hidden hearing loss (NIHHL) has been proposed to cover the functional deficits in hearing associated with noise-induced synaptopathy. Moreover, the potential functional deficit associated with the noise-induced synaptopathy has been largely attributed to the loss of auditory nerve fibers (ANFs) with a low spontaneous spike rate (SSR). As this group of ANFs is critical for coding at suprathreshold levels and in noisy background, coding-in-noise deficit (CIND) has been considered to be main consequence of the synaptopathy. However, such deficits have not been verified after a single, brief exposure to noise without PTS. In the present study, synaptopathy was generated by such noise exposure in both mice and guinea pigs. Responses to amplitude modulation (AM) were recorded at a high sound level in combination with masking to evaluate the existence of CINDs that might be associated with loss of low-SSR ANFs. An overall reduction in response amplitude was seen in AM-evoked compound action potential (CAP). However, no such reduction was seen in the scalp-recorded envelope following response (EFR), suggesting a compensation due to increased central gain. Moreover, there was no significant difference in masking effect between the control and noise groups. The results suggest that either there is no significant CIND after the synaptopathy we created, or the AM response tested with our protocol was not sufficiently sensitive to detect such a deficit; far-field EFR is not sensitive to cochlear pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".