Residential Air Pollution and Otitis Media During the First Two Years of Life
Bibliographic record
Abstract
BACKGROUND: : Otitis media is the leading reason young children receive antibiotics or visit a physician. We evaluated the impact of ambient air pollution on outpatient physician visits for otitis media in a population-based birth cohort. METHODS: : All children born in southwestern British Columbia during 1999-2000 were followed until the age of 2 years. Residential air pollution exposures were estimated for the first 24 months of life by inverse-distance weighting of monitor data (CO, NO, NO2, O3, PM2.5, PM10, SO2), temporally adjusted land-use regression models (NO, NO2, PM2.5, black carbon, woodsmoke), and proximity to roads and point sources. We used generalized estimating equations to longitudinally assess the relationship between physician visits for otitis media (ICD-9) and average pollutant exposure in the 2 months prior to the visit, after adjustment for covariates. RESULTS: : Complete exposure and risk-factor data were available for 45,513 children (76% of all births). A total of 42% of subjects had 1 or more physician visits for otitis media during follow-up. Adjusted estimates for NO, PM2.5, and woodsmoke were consistently elevated (eg, relative risk of 1.10 [95% confidence interval = 1.07-1.12] per interquartile range [IQR] increase in NO; 1.32 [1.27-1.36] per IQR increase in days of woodsmoke exposure). No increased risks were observed for the remaining pollutants (eg, 1.00 [0.98-1.03] per IQR increase in PM10; 0.99 [0.97-1.01] per IQR increase in black carbon). CONCLUSIONS: : Modest but consistent associations were found between some measures of air pollution and otitis media in a large birth cohort exposed to relatively low levels of ambient air pollution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".