Exposure to insulating materials and risk of coronary artery diseases: a cross-sectional study
Bibliographic record
Abstract
Background Although previous reports link exposure to insulating materials with an increased risk of mesothelioma and chronic respiratory diseases, studies evaluating their associations with the risk of coronary artery diseases (CAD) are lacking. Aims We aimed at evaluating the associations between exposure to insulating materials and the 10-year risk of CAD among insulators. Methods In this cross-sectional study, we recruited 643 adults (≥18 years), full-time insulators from the Local 110 Heat and Frost Insulators and Allied Workers Union in Edmonton, Alberta. We obtained demographic information, personal and family history, and job-exposure history, including experience (years) and types of exposure to insulating materials. Clinical profiling including Framingham risk scores (FRS) was assessed. Results Of all insulators, 89% were men (mean ± SD age: 47 ± 12 years), 27% had a parental history of cardiac diseases, and 22% had a comorbid chronic respiratory disease. In total, 53% reported exposure to asbestos, while 61, 82, and 94% reported exposure to ceramic fibers, fiberglass, and mineral fibers, respectively. In single-exposure multivariable regression models adjusted for experience, marital status, and body mass index (BMI), asbestos was found to be associated with higher FRS (β: 1.004; 95%CI: 0.003–2.00). The association remained consistent in multi-exposure models and a higher association was found between asbestos exposure and FRS among insulators with comorbid chronic respiratory disease. Conclusion Our study demonstrates that apart from cancer and chronic respiratory diseases, asbestos exposure may also have a cardiac effect, thus warranting the need for systematic surveillance to protect workers from the adverse effects of these materials.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".