Noise Damage Accelerates Auditory Aging and Tinnitus: A Canadian Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Age-related hearing loss (ARHL) is the third most challenging disability in older adults. Noise is a known modifiable risk factor of ARHL, which can drive adverse health effects. Few large-scale studies, however, have shown how chronic noise exposure (CNE) impacts the progression of ARHL and tinnitus. STUDY DESIGN: Retrospective large-scale study. SETTING: Audiology clinical practice. PATIENTS: In this study, 928 individuals aged 30-100 years without (n=497) or with the experience of CNE (n=431) were compared in their hearing assessments and tinnitus. In order to only investigate the impact of CNE on ARHL and tinnitus, people with other risk factors of hearing loss were excluded from the study. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Noise damage was associated with a greater ARHL per age decades (pure-tone average(PTA)0.5-4kHz alterations 19.6-70.8 dB vs. 8.0-63.2 dB, ≤0.001), an acceleration of developing a significant ARHL at least by two decades (PTA0.5-4kHz 33.4 dB at 50-59yr vs. 28.2 dB at 30-39yr, ≤0.001), and an increased loss of word recognition scores (total average 84.7% vs. 80.0%, ≤0.001). Significant noise-associated growth in the prevalence of tinnitus also was shown, including more than a triple prevalence for constant tinnitus (28.10% vs. 8.85%, ≤0.001) and near to a double prevalence for intermittent tinnitus (19.10% vs. 11.10%, ≤0.001). Noise also resulted in the elevation of the static compliance of the tympanic membrane throughout age (total average 0.61 vs. 0.85 mmho, ≤0.001). CONCLUSIONS: Our findings emphasize the significant contribution of CNE in auditory aging and the precipitation of both ARHL and tinnitus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".