Influence of Ambient Air Pollutant Sources on Clinical Encounters for Infant Bronchiolitis
Bibliographic record
Abstract
RATIONALE: Data regarding the influence of ambient air pollution on infant bronchiolitis are few. OBJECTIVES: We evaluated the impact of several air pollutants and their sources on infant bronchiolitis. METHODS: Infants in the Georgia Air Basin of British Columbia with an inpatient or outpatient clinical encounter for bronchiolitis (n = 11,675) were matched on day of birth to as many as 10 control subjects. Exposure to particulate matter with a diameter of 2.5 mum or less (PM(2.5)), PM(10), NO(2)/NO, SO(2), CO, and O(3) were assessed on the basis of a regional monitoring network. Traffic exposure was assessed using regionally developed land use regression (LUR) models of NO(2), NO, PM(2.5), and black carbon as well as proximity to highways. Exposure to wood smoke and industrial emissions was also evaluated. Risk estimates were derived using conditional logistic regression and adjusted for infant sex and First Nations (Canadian government term for recognized aboriginal groups) status and for maternal education, age, income-level, parity, smoking during pregnancy, and initiation of breastfeeding. MEASUREMENTS AND MAIN RESULTS: An interquartile increase in lifetime exposure to NO(2), NO, SO(2), CO, wood-smoke exposure days, and point source emissions score was associated with increased risk of bronchiolitis (e.g., adjusted odds ratio [OR(adj)] NO(2), 95% confidence interval [CI], 1.12, 1.09-1.16; OR(adj) wood smoke, 95% CI, 1.08, 1.04-1.11). Infants who lived within 50 meters of a major highway had a 6% higher risk (1.06, 0.97-1.17). No adverse effect of increased exposure to PM(10), PM(2.5), or black carbon, was observed. Ozone exposure was negatively correlated with the other pollutants and negatively associated with the risk of bronchiolitis. CONCLUSIONS: Air pollutants from several sources may increase infant bronchiolitis requiring clinical care. Traffic, local point source emissions, and wood smoke may contribute to this disease.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".