Respiratory illness in asbestos contaminated sites: the role of environmental exposure
Bibliographic record
Abstract
Over the last few years, advances in the assessment of the burden of disease caused by asbestos have taken place, even though the health impact of this agent has been investigated for about a century. The International Agency for Research on Cancer has recently concluded that there is a causal role of asbestos in laryngeal and ovarian cancer, in addition to the previously assessed links with lung cancer and mesothelioma [1]. A European Respiratory Society position paper [2] endorsed the notion of an ongoing mesothelioma epidemic that is expected to peak around 2020 in Western European countries, where asbestos consumption reached its maximum in the mid-1970s and subsequently decreased until its final ban. Currently, Asia and parts of Eastern Europe use ∼70% of the world’s production of this mineral, which is also extracted and employed with varying degrees of control and restrictions in Canada, and in parts of Africa and Latin America. Globally, mesothelioma occurrence in the years 1994–2008 was ∼174,000 cases reported in 56 countries and ∼40,000 more cases estimated in countries with the presence of asbestos in the absence of mesothelioma reporting [3]. The health effects of environmental exposure caused by residence in neighbourhoods with asbestos quarries and factories are one of the priority topics for scientific research in this domain. This problem was originally reported by Wagner et al. [4] in 1960. The issue has since been extensively investigated, bringing to light a range of outcomes and exposure circumstances that have …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".