Perinatal air pollution exposure and development of asthma from birth to age 10 years
Bibliographic record
Abstract
Within-city variation in air pollution has been associated with childhood asthma development, but findings have been inconsistent. We examined whether perinatal air pollution exposure affected asthma onset during "pre-school and "school age" periods in a population-based birth cohort.65,254 children born between 1999 and 2002 in the greater Vancouver metropolitan region were followed until age 10 years using linked administrative health databases. Asthma cases were sex- and age-matched to five randomly chosen controls. Associations between exposure to air pollutants estimated with different methods (interpolation (inverse-distance weighted (IDW)), land use regression, proximity) and incident asthma during the pre-school (0-5 years) and school age (6-10 years) periods were estimated with conditional logistic regression.6948 and 1711 cases were identified during the pre-school and school age periods, respectively. Following adjustment for birthweight, gestational period, household income, parity, breastfeeding at discharge, maternal age and education, asthma risk during the pre-school years was increased by traffic pollution (adjusted odds ratio using IDW method per interquartile increase (95% CI): nitric oxide 1.06 (1.01-1.11), nitrogen dioxide 1.09 (1.04-1.13) and carbon monoxide 1.05 (1.01-1.1)). Enhanced impacts were observed amongst low-term-birthweight cases. Associations were independent of surrounding residential greenness.Within-city air pollution variation was associated with new asthma onset during the pre-school years.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".