Audiometric thresholds and portable digital audio player user listening habits
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between portable digital audio player listening behaviours and (1) measured sound pressure levels, (2) audiometric measures, (3) self-reported hearing loss symptoms. DESIGN: A questionnaire to evaluate listening behaviours, including self-reported hearing loss symptoms and listening duration/volume settings. Multivariate regression analysis was used to determine the relationship between these variables, audiometric evaluation, calculated exposure levels, Lex(8hr), and measured sound pressure levels, Leq(32sec). STUDY SAMPLE: This study included 103 males and 134 female subjects aged 10 to 17 years. RESULTS: Calculated Lex(8hr) and measured Leq(32sec) levels increased with age and self-reported usage time. Audiometric thresholds averaged over 4 and 8 kHz were higher when usage exceeded five years as compared to less than one year. Higher measured sound pressure levels were associated with worse audiometric thresholds at (0.5, 1, 2 kHz, averaged) and 4 kHz. Self-reported hearing loss symptoms were reported by 33% to 50% of subjects. CONCLUSIONS: In this cohort sample, our results support a statistical association between hearing acuity and (1) Self-reported weekly usage in hours; (2) Tightness of fit; (3) Years of usage; and (4) Measured sound pressure levels. Generalizing these results beyond the current sample would require additional research.
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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 | 0.005 |
| 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) | 0.004 | 0.001 |
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".