Update in Environmental and Occupational Lung Diseases 2013
Bibliographic record
Abstract
In this update, recent publications within the field of environmental and occupational medicine, from this journal and others, are highlighted. Effects of ambient air pollution on a diverse range of airways disease continue to be discovered, reiterating the impressive potency of this near-ubiquitous and largely anthropogenic toxicant. Air pollution is now recognized as the third leading cause of disability-adjusted life years due to chronic respiratory disease globally (1). Studies in the past year have significantly strengthened the link between air pollution exposure and adverse health outcomes including mortality (2–4), lung cancer (4–6), infections (7–9), and obstructive lung disease (including asthma) (10, 11). Important underlying mechanistic details were illuminated in several animal models (12–17). The association between poor indoor air quality and lung diseases is a topic of increasing interest and was investigated in several reports in 2013 (18–21). Controlled human exposure experiments were in the spotlight, in part because of ethical concerns about the justification of this approach. An important position paper by leading researchers and a biomedical ethicist strongly supports the usefulness of controlled exposures, which continue to provide new insights into adverse health effects of different pollutants (22). Asbestos exposure is a well-recognized risk factor for pleural mesothelioma, and two epidemiology studies were published that explored the interaction between exposure to asbestos, other mineral fibers, and cigarette smoking in mesothelioma, asbestosis, and lung cancer (23, 24). Arsenic is increasingly recognized as a groundwater contaminant, with potential effects on lung development and function. An important paper provided strong evidence that even low levels of arsenic exposure are associated with reduced lung function (25). The adverse health effects of global warming are becoming apparent, and one paper provided a sobering reminder that elevated ambient temperatures are a strong risk factor for respiratory morbidity (26). Unfortunately, the adverse effects of environmental and occupational exposures on lung health occur disproportionately in ethnic minorities and those with lower socioeconomic status, underscoring the urgent need to address these important health disparities (27). Although our understanding of the adverse effects of environmental and occupational exposures on lung health continues to expand, more research is needed to understand mechanisms involved and discover the optimal public health measures to mitigate risk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.030 |
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".