Late Breaking Abstract - Analysis of chronic occupational exposure in non-smoking insulators
Bibliographic record
Abstract
Cardiovascular and respiratory diseases are among global leading causes of mortality. Insulators are at risk of these illnesses because of chronic exposure to asbestos and silica. Many insulators are habitual smokers, which confounds etiological diagnosis of disease. In this study, a retrospective analysis of non-smoking insulators examined the direct effects of occupational exposure. Unionized non-smoking insulators (n = 301) were recruited through a longitudinal surveillance program in Alberta (41 ± 14 years, 29 ± 5 kg/m2 BMI). Family, health, and work history, using validated questionnaires, and full physical assessments were collected. Insulators were assessed by pulmonary function tests (PFTs), chest X-rays, Framingham cardiac risk scores, and chronic obstructive pulmonary disease (COPD) assessment test (CAT) scores. For controls, a cohort of Royal Canadian Mounted Police (RCMP) officers was assessed (37 ± 9 years, 29 ± 6 kg/m2 BMI). Over half the insulators (54%) were exposed to asbestos in their working environment. No differences were found in PFT values from insulators and RCMP officers. While the CAT score for insulators (mean, 4 ± 4) was lower than that of RCMP (6 ± 5, p < 0.0001), more insulators exhibited lung abnormalities in chest X-rays than in RCMP (10% vs. 3%, p < 0.01). The Framingham risk score for non-smoking insulators averaged 10 ± 8% (normal range, 0-10%), with as many as 25% insulators showing values of >13%. COPD and asbestos-related lung diseases (ARLD) were found in 18% of insulators. Our findings suggest an emerging public health crisis in the health of insulators independent of smoking habits. More needs to be done to protect the health of workers and prevent the onset of lung 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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".