Mortality among workers exposed to asbestos in mining activities: 1940 - 2010
Bibliographic record
Abstract
Introduction: Asbestos is considered carcinogenic to humans. Brazil is among the four largest global producers. There are many international studies relating occupational exposure to asbestos. However, there are no studies of mortality among exposed workers in Brazil.This is the first study to evaluate mortality performed in our country on minning activity. Objectives: To investigate mortality among workers exposed to asbestos in mining activity. Methods: The death certificates of former employees with underlying cause of death were consulted. They were coded to ICD-10 for further descriptive analysis. Results: Of the 616 cases studied, it was possible to establish the cause of death in 429. The profile of the mortality in the population of asbestos-exposed workers was similar to the observed in the brazilian overall population. Concerning cancer of the lung and mesothelioma, the crude rate observed for the first is 7.25/100,000 individuals, and for the second is 1.04/100,000. To compare these values, the researchers referred to three Brazilian cities, to the European population, and to the data of the asbestos-exposed population of Canada. The numbers of the brazilian cities, from 1990 to 1993 one case of mesothelioma with a crude and standardized rate of 0.1/100,000 individuals and 198 cases of cancer of the lung with crude and standardized rates of 11.2 and 21.3, respectively, were observed. Conclusions: The mortality observed in the assessed population has a profile which is similar to the population where the workers come from, however the quality of information was a major limitation of this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".