Polycyclic Aromatic Hydrocarbons and Fatal Ischemic Heart Disease
Bibliographic record
Abstract
BACKGROUND: Several toxicologic and epidemiologic studies have produced evidence that occupational exposure to polycyclic aromatic hydrocarbons (PAH) is a risk factor for ischemic heart disease (IHD). However, a clear exposure-response relation has not been demonstrated. METHODS: We studied a relation between exposure to PAH and mortality from IHD (418 cases) in a cohort of 12,367 male asphalt workers from Denmark, Finland, France, Germany, Israel, The Netherlands and Norway. The earliest follow up (country-specific) started in 1953 and the latest ended in 2000, averaging 17 years. Exposures to benzo(a)pyrene were assessed quantitatively using measurement-driven exposure models. Exposure to coal tar was assessed in a semiquantitative manner on the basis of information supplied by company representatives. We carried out sensitivity analyses to assess potential confounding by tobacco smoking. RESULTS: Both cumulative and average exposure indices for benzo(a)pyrene were positively associated with mortality from IHD. The highest relative risk for fatal IHD was observed for average benzo(a)pyrene exposures of 273 ng/m or higher, for which the relative risk was 1.64 (95% confidence interval=1.13-2.38). Similar results were obtained for coal tar exposure. Sensitivity analysis indicated that even in a realistic scenario of confounding by smoking, we would observe approximately 20% to 40% excess risk in IHD in the highest PAH-exposure categories. CONCLUSIONS: Our results lend support to the hypothesis that occupational PAH exposure causes fatal IHD and demonstrate a consistent exposure-response relation for this association.
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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 | 0.000 | 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".