Testing for evidence of cochlear synaptopathy in normal-hearing young adults with varying noise exposure history
Bibliographic record
Abstract
Loss of high-threshold cochlear synapses following mild noise exposure is suggested to degrade amplitude modulation (AM) encoding in cases where hearing thresholds are normal. However, the relationship of AM encoding to noise exposure history has not been consistent in previous studies. We investigated this relationship in young adults with normal audiograms in two studies using different methods. Study 1 (N = 25) measured the ~80 Hz electrophysiological envelope following response (EFR) and behavioural AM detection thresholds. Both measures were taken in quiet and in a narrowband background noise designed to attenuate low-threshold synapse contributions. When subjects were divided into two groups based on their noise exposure history, subjects with more noise exposure had smaller EFRs (p = 0.0198). AM detection was also poorer in these subjects, but this difference fell short of significance (p =0.067). Study 2 (ongoing) also measures the EFR but employs an additional wider band of background noise to attenuate possible off-frequency contributions of low threshold fibers to AM coding. In addition, AM discrimination is tested instead of AM detection. The question is whether these modifications will reveal a more robust effect of noise exposure history on AM coding than did Study 1. [Work supported by NSERC of Canada.]
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".