Middle ear abnormalities at age 5 years in relation with early onset otitis media and number of episodes, in the Inuit population of Nunavik, Quebec, Canada
Bibliographic record
Abstract
Otitis media (OM) and their sequelae are a major health issue in the Inuit population of Nunavik, Quebec. Hypotheses of the study were: (i) early onset OM leads to repeated OM; (ii) repeated OM episodes leads to middle ear abnormalities (MEA) at age 5 years, (iii) pneumococcal conjugate vaccines (PCVs) may reduce multiple OM and MEA. Immunisation cards, medical records and audiology screening tests at age 5 years in a sample of 610 children born in 1994-2010 in 3 communities were reviewed. Children were classified into three categories using a score based on audiology screening tests: no abnormality, minor, or major MEA. The average number of OM episodes before age 5 years was 5.0 and 30% had minor and 17% major MEA at age 5 years. Community residency predicted both frequent (≥ 8) OM episodes and MEA. Early onset OM (age <6 months) was a predictor of frequent OM (RR = 1.71; 95%CI: 1.50-1.95) whereas PCV (≥1 dose ≥ age 2 months) has no significant effect. Frequent OM episodes were associated with major MEA (RR = 2.16; 95%CI: 1.20-3.85). Although associations were not statistically significant, there was a trend towards a protective effect of PCV administration on frequent OM and minor MEA, but not major MEA. In conclusion, results support an association between early onset OM, frequent OM and MEA that could represent a causal pathway.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".