Risk of Developing Mesothelioma due to Neighborhood Exposure to Asbestos
Bibliographic record
Abstract
Routes of asbestos exposure consist of occupational and non-occupational exposures, and furthermore the latter is classified as para-occupational, neighborhood or true general environmental exposure. Consequently, in order to evaluate health risk caused by neighborhood exposure to asbestos, it is necessary to exclude risk due to the other exposure routes from overall risk. We reviewed epidemiological studies on the relationship between neighborhood asbestos exposure and risk of mesothelioma. In studies on a crocidolite mine in South Africa and a chrysotile mine in Canada, occupational exposure was not excluded. In studies on a crocidolite mine in Australia and an asbestos manufacturing factory in U.S.A., risk caused by non-occupational exposure was evaluated, but the risk was not classified as para-occupational and neighborhood exposures. In a study on an asbestos cement factory in Italy, first, occupational and para-occupational exposures were excluded, and next, the incidence rate of mesothelioma in neighborhood residents was calculated, so that risk caused by neighborhood exposure could be evaluated. In case-control studies in Italy, South Africa, three European countries and the U.K., risks caused by occupational, para-occupational and neighborhood exposures were evaluated separately. As a whole, relative risk (RR) of neighborhood exposure in crocidolite and amosite mines was about 10 to 30 and RR in major asbestos factories was about 5 to 20. On the other hand, statistically significant RR of neighborhood exposure was not observed in chrysotile mines and some asbestos facilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".