Longitudinal analysis of chronic occupational exposure in insulators
Bibliographic record
Abstract
Cardiovascular and respiratory diseases are among the global leading causes of mortality. Insulators are at risk of these illnesses because of chronic exposure to asbestos and silica. In this study, a longitudinal analysis was carried out to determine the effect of asbestos exposure on the lung and cardiac health of insulators. Unionized insulators (n = 894) were assessed in Alberta (45 ± 14 years) and full physical assessments, family, health, and work histories were collected along with pulmonary function tests, chest X-rays, Framingham cardiac risk scores, and COPD assessment test (CAT) scores. Subjects were followed up, approximately every 2 years, with 894 in 1st, 426 in 2nd, 149 in 3rd, and 21 in 4th visits. At baseline, 63% of the cohort was exposed to asbestos, and 66% had a history of smoking. FEV1 and FEV1/FVC declined over time for insulators, with an additional decrement for smokers. CAT scores were elevated in smokers and insulators exposed to asbestos that used some or no personal protection equipment. Framingham scores increased each year independent of age. Baseline asbestos exposure and smoking were significant risk factors for increased Framingham risk scores. The incidence rate of developing X-ray-detected lung abnormalities, COPD, and asbestos-related lung diseases were 5.3, 4.4, and 0.8 per 100 person-year, respectively. Our findings show significant declines in lung function, increased risk of cardiac diseases, and elevated rates of lung diseases over a period of 4 ± 1 years in insulators. This study is still in progress to increase sample size, but already we have found evidence that more needs to be done to protect the health of workers and prevent the onset of lung and cardiac diseases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".