Tinnitus prevalence and associations with leisure noise exposure among Canadians, aged 6 to 79 years
Bibliographic record
Abstract
Objective To examine the association between individual, cumulative leisure noise exposure (CLNE), acceptable yearly exposure (AYE) and tinnitus among a nationally representative sample of Canadians.Design In-person household questionnaires were used to evaluate leisure noise exposure across age, sex, household income and tinnitus: ever experienced, previous year, frequent, bothersome. High (≥85 dBA, LEX), low (<85 dBA, LEX) CLNE and AYEs were defined according to occupational limits.Study sample A randomised sample of 10,460 respondents, aged 6–79, completed questionnaires between 2012 and 2015. Results: Tinnitus prevalence was highest among young adults and teenagers (50% for both). Frequent and bothersome tinnitus afflicted one-third and 18.1% of Canadians, respectively. Men had higher tinnitus prevalence while women had increased bothersome tinnitus. For most ages, elevated tinnitus odds ratios (ORs) were associated with: (1) high, low CLNE and AYEs ≥1 and, (2) high exposure from individual sources: loud home/car stereo listening, power tools, gasoline engines, highway motorcycle/snowmobile driving. Loud personal listening device usage was associated with tinnitus ORs doubling (ages 6–11) and ≥5 or <5 years of loud usage, with increased tinnitus ORs (ages 12–19).Conclusion Community and/or school-based educational outreach would be beneficial to increase awareness of loud leisure noise exposure and tinnitus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".