Consequences and Mechanisms of Noise‐Induced Cochlear Synaptopathy and Hidden Hearing Loss, With Focuses on Signal Perception in Noise and Temporal Processing
Bibliographic record
Abstract
Noise-induced synaptopathy and relevant hidden hearing loss (NIS and NIHHL) have been a hot topic in hearing research for almost 15 years. The progress is summarized in this review to address the reversibility of the synaptic damage after the initial loss, and the role of functional deficit in the repaired synapses as the reason for hearing impairment in addition to the deafferentiation caused by the synaptic loss, per se. The evidence supporting the synaptic repair is summarized. It is pointed out that coding-in-noise deficit (CIND) may not be the major problem of NIS and NIHHL, since solid evidence supporting the existence of this deficit is not available even in animal studies, as well as in in human reports. Rather, temporal processing deficits are clearly demonstrated in subjects with NIS and potentially NIHHL. The idea of CIND as the major concern in NIHHL is proposed based upon the functional categorization of the auditory nerve (ANF) by spontaneous rate and the biased loss of the ribbon synapses innervation the low-SR ANF. The limitation of this hypothesis is discussed in detail. The review also addresses the difficulty of translating animal data to humans and the need for new research in the future.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".