Hypertension in noise-exposed sawmill workers: a cohort study
Bibliographic record
Abstract
OBJECTIVE: To investigate the hypothesised association between exposure to high levels of noise and risk of hypertension using quantitative exposure assessment and administrative health data. METHODS: This study followed a cohort of 10 872 sawmill workers in British Columbia from 1991 to 1998. Subjects were linked with provincial hospital discharge, outpatient and vital status databases. Cases were males who died, had at least one hospital admission, or who had three doctor visits within 70 days, for hypertension (ICD-9 codes 401-405). We used four exposure metrics: cumulative exposure, and duration of exposure above thresholds of 85 dBA, 90 dBA and 95 dBA. Relative risks were estimated using Poisson regression with the low-exposure group as controls and adjusting for age, ethnicity and calendar period. RESULTS: 828 cases were identified. The results showed a monotonic increase in hypertension incidence with cumulative exposure. The risk in the highest exposed population was 32% higher than baseline. Similar results were found using duration of exposure metrics. The highest relative risk was 1.5 in workers exposed for more than 30 years at 85 dBA. Exposure-response trends were statistically significant. CONCLUSIONS: The risk of hypertension was positively associated with noise exposure above 85 dB.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".