Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to offer a concise summary of current knowledge regarding hidden hearing loss (HHL) and to describe the variety of mechanisms that contribute to its development. We will also discuss the various diagnostic tools that are available as well as future directions. RECENT FINDINGS: Hidden hearing loss often also called cochlear synaptopathy affects afferent synapses of the inner hair cells. This description is in contrast to traditional models of hearing loss, which predominantly affects auditory hair cells. In HHL, the synapses of nerve fibres with a slow spontaneous firing rate, which are crucial for locating sound in background noise, are severely impaired. In addition, recent research suggests that HHL may also be related to cochlear nerve demyelination. Noise exposure causes loss of myelin sheath thickness. Auditory brainstem response, envelope-following response and middle-ear muscle reflex are promising diagnostic tests, but they have yet to be validated in humans. SUMMARY: Establishing diagnostic tools for cochlear synaptopathy in humans is important to better understand this patient population, predict the long-term outcomes and allow patients to take the necessary protective precautions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".