Early-life Otitis Media and Incident Atopic Disease at School Age in a Birth Cohort
Bibliographic record
Abstract
BACKGROUND: Otitis media is a common and costly disease that peaks in early childhood. Recent reviews concluded that the relationship between otitis media and atopy is not well understood, and that further research is warranted. METHODS: Logistic regression was used to analyze data from a German Birth Cohort (n = 1690; born 1997–1999). Parental questionnaires were used to assess children for physician-diagnosed otitis media throughout the first 2 years of life and for incident atopic disease (asthma, allergic rhinitis, and eczema) during the sixth year of life. Odds ratios were adjusted for gender, older siblings, city, parental education, breast-feeding, and daycare. Parallel analyses were completed for the full birth cohort and for a population subset with atopic mothers. RESULTS: The adjusted odds of asthma were elevated for children with early-life otitis media, but were statistically significant only for those children with at least 3 episodes (adjusted odds ratio: 4.26 [95% confidence interval: 1.34–13.6]). Associations between early-life otitis media and allergic rhinitis were largely inconsistent. There was a positive association between early-life otitis media and late-onset allergic eczema (≥2 episodes: 2.68 [1.35–5.33], ≥3 episodes: 3.84 [1.80–8.18]). Similar results were found for the maternal atopy subgroup but with greater effect estimates. CONCLUSIONS: Children diagnosed with otitis media during infancy were at greater risk for developing late-onset allergic eczema and asthma during school age, and associations were stronger for frequent otitis. These results indicate that frequent otitis media during infancy may predispose children to atopic disease in later 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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".