GABAergic Inhibition Underpins Hidden Hearing Loss
Bibliographic record
Abstract
Hearing involves both peripheral and central processing mechanisms that range from simple detection of sounds to complex processing that allows speech comprehension. Hidden hearing loss refers to a condition in which individuals show normal hearing thresholds on an audiogram but experience difficulties in more complex auditory tasks (Salvi et al., 2018; Gallun, 2021). Aging and noise exposure are contributing factors to hidden hearing loss (Salvi et al., 2018). The substrates responsible for this type of hearing loss are unclear, because deficits are observed all along the auditory pathway throughout aging. Specifically, aging has been shown to lead to loss of cochlear hair cells and synapses, damage to the auditory nerve, and impairment in the central auditory system. Notably, although there are no cortical volume changes in the human auditory cortex associated with age-related hearing loss, evidence indicates that GABA concentrations, as measured by MR spectroscopy, decrease with age and hearing thresholds (Ouda et al., 2015; Dobri and Ross, 2021). A useful measure to get an insight into central auditory deficits, specifically those occurring in the inferior colliculus and auditory cortex, is the binaural masking level difference (BMLD; Gilbert et al., 2015). As we have all experienced, the presence of noise can make it difficult to detect a target sound. But a target that is masked by noise will become detectable (unmasked) if the target and noise arise from different locations. Spatial segregation produces a detectable interaural time difference that can be mimicked in experimental settings by inverting the phase of the target sound presented to each ear (Gilbert et al., 2015). The inputs to the … Correspondence should be addressed to Daniel Paromov at daniel.paromov{at}umontreal.ca.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".