Occupational noise exposure and ischaemic heart disease mortality
Bibliographic record
Abstract
AIMS: To investigate the hypothesis that long term exposure to excessive noise can increase the risk of ischaemic heart disease. METHODS: A case-control design, nested within a cohort of nuclear power workers employed at two sites in England over the period 1950-98, was used. Cases were men who died from ischaemic heart disease (ICD-9: 410-414) aged 75 or under; each was matched to a surviving control of the nearest age who joined the same site at the same time. Personal noise exposure was assessed retrospectively for each man by hygienists using (1) company work histories, (2) noise survey records from 1965-98, and (3) judgements about likely use of hearing protection devices. Men were classified into four groups according to their cumulative exposure to noise, with men whose exposure at the company never exceeded 85dB(A) for at least one year being considered "unexposed". Risks were compared via odds ratios (ORs) using conditional logistic regression and adjusted for systolic and diastolic blood pressure, height, BMI, and smoking, as measured at recruitment to the company. RESULTS: Analysis was based on 1101 case-control pairs. There was little difference between the exposure groups at recruitment. There was no evidence of increased risk at site A: the ORs for ischaemic heart disease mortality among low, medium, and high exposure categories, compared to unexposed men, being 1.04, 1.00, and 0.77. The corresponding ORs (95% CIs) at site B were 1.15 (0.81-1.65) 1.45 (1.02-2.06), and 1.37 (0.96-1.96). When the comparison was confined to men with at least five years of employment, these dropped to 1.07 (0.64-1.77), 1.33 (0.88-2.01), and 1.21 (0.82-1.79) respectively. CONCLUSIONS: The authors did not find statistically robust evidence of increased risk but the estimates at site B are consistent with those in a major cohort study. A strength of the present study is that the validity of noise estimation at site B has been demonstrated elsewhere.
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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 | 0.002 |
| 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) | 0.002 | 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".