Asymmetry in noise-induced hearing loss: Evaluation of two competing theories
Bibliographic record
Abstract
Competing theories exist about why asymmetry is observed in noise-induced hearing loss (NIHL). We evaluated these theories using a cohort of young workers studied over 16 years. The study aim was to describe and evaluate patterns of hearing loss and asymmetry by gender, agricultural exposure and gunfire exposure. This was a secondary analysis of data collected from young adults during follow-up of a randomized controlled trial. This follow-up study evaluated long-term effects of a hearing conservation intervention for rural students. The sample consisted of 392 of 690 participants from the original trial. In total, 355 young adults (aged 29-33 years) completed baseline and follow-up noise exposure surveys and clinical audiometric examinations. Data are displayed graphically as thresholds by frequency and ear and degree of asymmetry between ears (left minus right). In the primary group comparisons, low and high frequency averages and mean high frequency asymmetry were analyzed using mixed linear models. At frequencies >2000 Hz, men showed more hearing loss, with greater asymmetry and a different asymmetry pattern, than women. For men with documented hearing loss, there was a trend toward increasing asymmetry with increasing levels of hearing loss. Asymmetry at high frequencies varied substantially by level of shooting exposure. While "head shadowing" is accepted as the primary explanation for asymmetric hearing loss in the audiologic and related public health literature, our findings are more consistent with physiological differences as the primary cause of asymmetric hearing loss, with greater susceptibility to NIHL in the left ear of men.
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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 | 0.118 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".