Air pollution and development of asthma, allergy and infections in a birth cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it