How Much, How Long, What, and Where: Air Pollution Exposure Assessment for Epidemiologic Studies of Respiratory Disease
Bibliographic record
Abstract
Epidemiology has played an important role in the understanding of air pollution as a risk factor for respiratory disease and in the evidence base for air quality standards. With the widespread availability of genetic information and increasingly sophisticated measurements of molecular markers of adverse effects, there is a need for more specific and precise assessment of exposure to maximize the potential information to be derived from epidemiologic studies. Here advances in air pollution exposure assessment and their applications to studies of respiratory disease are reviewed, with a focus on recent studies of traffic-related air pollution and asthma. Although continuous measurements of personal exposures for all study subjects for a complete study period might be considered the desired "gold standard" for exposure, this is rarely, if ever, achieved due to feasibility constraints. Given this, exposure is typically estimated using models. Recent applications of geospatial (e.g., land use regression) models to studies of respiratory disease have made possible new study designs focused on spatial variability in exposure within urban areas and have provided new insights into the potential role of traffic-related air pollution (TRAP) as a risk factor for the development of childhood asthma. Substantial uncertainty remains, however, regarding what agent(s) within TRAP might be responsible for the observed associations. Future research will require increasing the specificity of exposure assessment to identify the potential roles of individual air pollution components, to elucidate potential mechanisms, and to facilitate studies of mixtures and gene-air pollution interactions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".