Otitis media incidence and risk factors in a population-based birth cohort
Bibliographic record
Abstract
BACKGROUND: Otitis media is the main reason young children receive antibiotics and is the leading reason for physician visits. OBJECTIVE: To characterize the incidence, recurrence and risk factors for otitis media in a population-based birth cohort. METHODS: All children born in southwestern British Columbia during 1999 to 2000 were followed until the age of three years. Otitis media was defined using The International Classification of Diseases, Ninth Revision coding of physician visits, and linked with antibiotic prescription data. Information on sex, birth weight, gestational age, Aboriginal status, maternal age, older siblings, maternal smoking during pregnancy, breastfeeding initiation, neighbourhood income, female education and rural residence were obtained from vital statistics, birth hospitalizations, perinatal registry and census data. RESULTS: Complete risk factor information was available for 50,474 children (86% of all births). Nearly one-half of the children (48.6%) had one or more physician visits for otitis media during follow-up, and 3952 children (7.8%) met the definition for recurrent otitis media. Of the children with at least three visits during follow-up (n=7571), 73% had their initial visit during the first year of life. Aboriginal status, maternal age younger than 20 years, male sex and older siblings were the strongest risk factors identified in the adjusted conditional logistic regression models. DISCUSSION: The present study established a population-based birth cohort by linking multiple administrative databases to characterize the incidence of and risk factors for otitis media. Although the incidence of otitis media is generally low in southwestern British Columbia, important risk factors continue to be young maternal age, mothers who smoke during pregnancy and children with Aboriginal ancestry.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".