Coding deficits in hidden hearing loss induced by noise: the nature and impacts
Bibliographic record
Abstract
Hidden hearing refers to the functional deficits in hearing without deterioration in hearing sensitivity. This concept is proposed based upon recent finding of massive noise-induced damage on ribbon synapse between inner hair cells (IHCs) and spiral ganglion neurons (SGNs) in the cochlea without significant permanent threshold shifts (PTS). Presumably, such damage may cause coding deficits in auditory nerve fibers (ANFs). However, such deficits had not been detailed except that a selective loss of ANFs with low spontaneous rate (SR) was reported. In the present study, we investigated the dynamic changes of ribbon synapses and the coding function of ANF single units in one month after a brief noise exposure that caused a massive damage of ribbon synapses but no PTS. The synapse count and functional response measures indicates a large portion of the disrupted synapses were re-connected. This is consistent with the fact that the change of SR distribution due to the initial loss of low SR units is recovered quickly. However, ANF coding deficits were developed later with the re-establishment of the synapses. The deficits were found in both intensity and temporal processing, revealing the nature of synaptopathy in hidden hearing loss.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".