Exposure to loud noise and risk of vestibular schwannoma: results from the INTERPHONE international case‒control study
Bibliographic record
Abstract
Objective Studies of loud noise exposure and vestibular schwannomas (VS) have shown conflicting results. The population-based INTERPHONE case‒control study was conducted in 13 countries during 2000-2004. In this paper, we report the results of analyses on the association between VS and self-reported loud noise exposure. Methods Self-reported noise exposure was analyzed in 1024 VS cases and 1984 matched controls. Life-long noise exposure was estimated through detailed questions. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using adjusted conditional logistic regression for matched sets. Results The OR for total work and leisure noise exposure was 1.6 (95% CI 1.4-1.9). OR were 1.5 (95% CI 1.3-1.9) for only occupational noise, 1.9 (95% CI 1.4-2.6) for only leisure noise and 1.7 (95% CI 1.2-2.2) for exposure in both contexts. OR increased slightly with increasing lag-time. For occupational exposures, duration, time since exposure start and a metric combining lifetime duration and weekly exposure showed significant trends of increasing risk with increasing exposure. OR did not differ markedly by source or other characteristics of noise. Conclusion The consistent associations seen are likely to reflect either recall bias or a causal association, or potentially indicate a mixture of both.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".