Cochlear Synaptopathy and Noise-Induced Hidden Hearing Loss
Bibliographic record
Abstract
Recent studies on animal models have shown that noise exposure that does not lead to permanent threshold shift (PTS) can cause considerable damage around the synapses between inner hair cells (IHCs) and type-I afferent auditory nerve fibers (ANFs). Disruption of these synapses not only disables the innervated ANFs but also results in the slow degeneration of spiral ganglion neurons if the synapses are not reestablished. Such a loss of ANFs should result in signal coding deficits, which are exacerbated by the bias of the damage toward synapses connecting low-spontaneous-rate (SR) ANFs, which are known to be vital for signal coding in noisy background. As there is no PTS, these functional deficits cannot be detected using routine audiological evaluations and may be unknown to subjects who have them. Such functional deficits in hearing without changes in sensitivity are generally called "noise-induced hidden hearing loss (NIHHL)." Here, we provide a brief review to address several critical issues related to NIHHL: (1) the mechanism of noise induced synaptic damage, (2) reversibility of the synaptic damage, (3) the functional deficits as the nature of NIHHL in animal studies, (4) evidence of NIHHL in human subjects, and (5) peripheral and central contribution of NIHHL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".