Air pollution and development of asthma, allergy and infections in a birth cohort
Bibliographic record
Abstract
Few studies have addressed associations between traffic-related air pollution and respiratory disease in young children. The present authors assessed the development of asthmatic/allergic symptoms and respiratory infections during the first 4 yrs of life in a birth cohort study (n = approximately 4,000). Outdoor concentrations of traffic-related air pollutants (nitrogen dioxide PM(2.5), particles with a 50% cut-off aerodynamic diameter of 2.5 mum and soot) were assigned to birthplace home addresses with a land-use regression model. They were linked by logistic regression to questionnaire data on doctor-diagnosed asthma, bronchitis, influenza and eczema and to self-reported wheeze, dry night-time cough, ear/nose/throat infections and skin rash. Total and specific immunoglobulin (Ig)E to common allergens were measured in a subgroup (n = 713). Adjusted odds ratios (95% confidence intervals) per interquartile pollution range were elevated for wheeze (1.2 (1.0-1.4) for soot), doctor-diagnosed asthma (1.3 (1.0-1.7)), ear/nose/throat infections (1.2 (1.0-1.3)) and flu/serious colds (1.2 (1.0-1.4)). No consistent associations were observed for other end-points. Positive associations between air pollution and specific sensitisation to common food allergens (1.6 (1.2-2.2) for soot), but not total IgE, were found in the subgroup with IgE measurements. Traffic-related pollution was associated with respiratory infections and some measures of asthma and allergy during the first 4 yrs of life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".