A Quantitative Retrospective Exposure Assessment for Former Chrysotile Asbestos Miners and Millers from Baie Verte, NL, Canada
Bibliographic record
Abstract
Despite numerous studies of asbestos workers in the epidemiologic literature, there are very few cohort studies of chrysotile asbestos miners/millers that include high-quality retrospective exposure assessments. As part of the creation of the Baie Verte Miners' Registry in 2008, a two-dimensional job exposure matrix (JEM) was developed for estimating asbestos exposures for former chrysotile asbestos miners/millers. Industrial hygiene data collected between 1963 and 1994 were analysed to assess validity for use in a retrospective exposure assessment and epidemiologic study. Registered former employees were divided into 52 exposure groups (EGs) based on job title and department and mean asbestos concentrations were calculated for each EG. The resulting exposure estimates were linked to individual registrants' work histories allowing for the calculation of cumulative asbestos exposure for each registrant. The distribution of exposure for most EGs (82.6%) could be described as fitting a log-normal distribution, although variability within some EGs (55%) exceeded a geometric standard deviation (GSD) of 2.5. Overall, the data used to create EGs in the development of the JEM were deemed to be of adequate quality for estimating cumulative asbestos exposures for the former employees of the Baie Verte asbestos mine/mill. The variability between workers in the same job was often high and is an important factor to be considered when using estimates of cumulative asbestos exposure to adjudicate compensation claims. The exposures experienced in this cohort were comparable to those of other chrysotile asbestos miners/millers cohorts, specifically Italian and Québec cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".