Non-Malignant Respiratory Illnesses in Association with Occupational Exposure to Asbestos and Other Insulating Materials: Findings from the Alberta Insulator Cohort
Bibliographic record
Abstract
Many insulating materials are used in construction, although few have been reported to cause non-malignant respiratory illnesses. We aimed to investigate associations between exposures to insulating materials and non-malignant respiratory illnesses in insulators. In this cross-sectional study, 990 insulators (45 ± 14 years) were screened from 2011-2017 in Alberta. All participants underwent pulmonary function tests and chest radiography. Demographics, work history, and history of chest infections were obtained through questionnaires. Chronic obstructive pulmonary disease (COPD) was diagnosed according to established guidelines. Associations between exposures and respiratory illnesses were assessed by modified Poisson regression. Of those screened, 875 (88%) were males. 457 (46%) participants reported having ≥ 1 chest infection in the past 3 years, while 156 (16%) were diagnosed with COPD. In multivariate models, all materials (asbestos, calcium silicate, carbon fibers, fiberglass, and refractory ceramic fibers) except aerogels and mineral fibers were associated with recurrent chest infections (prevalence ratio [PR] range: 1.18-1.42). Only asbestos was associated with COPD (PR: 1.44; 95% confidence interval [CI]: 1.01, 2.05). Therefore, occupational exposure to insulating materials was associated with non-malignant respiratory illnesses, specifically, recurrent chest infections and COPD. Longitudinal studies are urgently needed to assess the risk of exposure to these newly implemented insulation materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".