Supra-threshold deficits in normal hearing military recruits exposed to impulse noise
Bibliographic record
Abstract
The aim of this study was to determine the effect of impulse noise exposure on various proxy measures of cochlear synaptopathy in young military recruits. A total of 27 military recruits with exposure to firearm and artillery noise and 13 non exposed participants were recruited. All presented with normal hearing thresholds and the presence of distortion product otoacoustic emissions (DPOAEs). The Noise Exposure Structured Interview (NESI) was used to quantify noise exposure. Speech perception in noise (SPiN), equivalent rectangular bandwidth (ERB) of auditory filters, auditory brainstem response wave I amplitude, wave I amplitude growth function, wave I/V amplitude ratio, wave V latency, wave V latency shift with ipsilateral noise, and the summating potential/action potential ratio of the electrocochleography were measured. In military participants, SPiN was worse, ERB at 4 kHz was larger, wave I amplitude at 75 dBnHL was reduced, and wave V latency was delayed. However, no significant correlations were observed between NESI and auditory measures, once multiplicity of tests was controlled for. These results suggest that military recruits may exhibit supra-threshold deficits, despite presenting with normal hearing thresholds and presence of DPOAEs. Future studies should include a measure of auditory filters in their test battery.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".