Risk of Mesothelioma Among Women Living Near Chrysotile Mines Versus US EPA Asbestos Risk Model: Preliminary Findings
Bibliographic record
Abstract
Introduction. The risk of asbestos diseases cannot be measured directly in populations with low level chrysotile asbestos exposure. Risk assessments must be used to extrapolate risks from past heavy industrial asbestos exposures to today's low chrysotile exposures. We tested the US Environmental Protection Agency (EPA) mesothelioma risk model in a population having experienced relatively high and mostly non-occupational chrysotile exposures. Methods. Female mesotheliomas first diagnosed from 1970 to 1989 in chrysotile asbestos mining districts (Asbestos and Thetford) were identified from the Quebec Tumour Registry and hospital records gathered throughout the province. Diagnoses were reviewed by three pathologists. An international expert panel estimated historical ambient exposure levels in these districts. A ‘time–area–job–family exposure’ matrix was derived from these estimates, occupational and cohabitation exposure estimates and a survey of 817 female residents. We applied the EPA mesothelioma incidence model to the population time–area–job–family exposure matrix and compared this predicted incidence with that actually observed. Results. Ambient airborne asbestos exposures were between 0.1 and 3 fibres/ml before 1970. The EPA asbestos risk model predicted 150 (range 30–750) female mesotheliomas in Asbestos, while only one case (peritoneal) was observed; 500 cases (range 100–2500) were predicted in Thetford Mines, while 10 cases (pleural) were observed. These large discrepancies cannot be explained by random or systematic errors.
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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 | 0.001 |
| 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) | 0.001 | 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 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".