Fine Particulate Matter and Emergency Room Visits for Respiratory Illness. Effect Modification by Oxidative Potential
Bibliographic record
Abstract
RATIONALE: Fine particulate air pollution (PM2.5; particulate matter 2.5 μm or less in diameter) is thought to contribute to acute respiratory morbidity in part through oxidative stress. OBJECTIVES: To examine the association between PM2.5 oxidative burden and emergency room visits for respiratory illnesses. METHODS: We conducted a case-crossover study in Ontario, Canada between 2004 and 2011, including 127,836 cases of asthma, 298,751 cases of chronic obstructive pulmonary disease, and more than 1.1 million cases of all respiratory illnesses. Daily air pollution data were collected from ground monitors, and city-level PM2.5 oxidative potential was measured on the basis of a synthetic respiratory tract lining fluid containing the antioxidants glutathione and ascorbate. Conditional logistic regression was used to estimate associations between air pollution concentrations and emergency room visits, adjusting for time-varying covariates. MEASUREMENTS AND MAIN RESULTS: Three-day mean PM2.5 concentrations were consistently associated with emergency room visits for all respiratory illnesses. Among children (<9 yr), each interquartile change (5.92 μg/m(3)) in 3-day mean PM2.5 was associated with a 7.2% (95% confidence interval, 4.2-10) increased risk of emergency room visits for asthma. Glutathione-related oxidative potential modified the impact of PM2.5 on emergency room visits for respiratory illnesses (P = 0.001) but only at low concentrations (≤10 μg/m(3)). Between-city differences in ascorbate-related oxidative potential did not modify the impact of PM2.5 on respiratory outcomes. CONCLUSIONS: Between-city differences in glutathione-related oxidative potential may modify the impact of PM2.5 on acute respiratory illnesses at low PM2.5 concentrations. This may explain in part how small changes in ambient PM2.5 mass concentrations can contribute to acute respiratory morbidity in low-pollution environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".