Spatiotemporal Variations in Ambient Ultrafine Particles and the Incidence of Childhood Asthma
Bibliographic record
Abstract
Abstract Rationale Little is known regarding the impact of ambient ultrafine particles (UFPs; <0.1 μm) on childhood asthma development. Objectives To examine the association between prenatal and early postnatal life exposure to UFPs and development of childhood asthma. Methods A total of 160,641 singleton live births occurring in the City of Toronto, Canada between April 1, 2006, and March 31, 2012, were identified from a birth registry. Associations between exposure to ambient air pollutants and childhood asthma incidence (up to age 6) were estimated using random effects Cox proportional hazards models, adjusting for personal- and neighborhood-level covariates. We investigated both single-pollutant and multipollutant models accounting for coexposures to particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) and NO2. Measurements and Main Results We identified 27,062 children with incident asthma diagnosis during the follow-up. In adjusted models, second-trimester exposure to UFPs (hazard ratio per interquartile range increase, 1.09; 95% confidence interval, 1.06–1.12) was associated with asthma incidence. In models additionally adjusted for PM2.5 and nitrogen dioxide, UFPs exposure during the second trimester of pregnancy remained positively associated with childhood asthma incidence (hazard ratio per interquartile range increase, 1.05; 95% confidence interval, 1.01–1.09). Conclusions This is the first study to evaluate the association between perinatal exposure to UFPs and the incidence of childhood asthma. Exposure to UFPs during a critical period of lung development was linked to the onset of asthma in children, independent of PM2.5 and NO2.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".