Burden of respiratory infections and otitis media in the Inuit population of Nunavik, Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Respiratory infections are a major health problem in the Inuit population of Nunavik, province of Quebec, Canada. OBJECTIVES: A study was undertaken to assess the burden of lower (LRTI) and upper respiratory tract infections (URTI) and otitis media (OM) and to explore some of their determinants including the pneumococcal conjugate vaccine (PCV) status. METHODS: The reference population includes children less than 5 years of age born in 1994-2010 and a sample of 825 children was selected for this study. Outpatient medical records were reviewed. Visits with a diagnosis of LRTI, URTI and OM were extracted. Univariate and multivariate statistical analyses were performed to identify predictors of disease risk. RESULTS: The average number of LRTI, URTI and OM episodes were, respectively, 2.6, 6.2 and 5.9 from birth up to the 5th birthday. Seasonal patterns were similar for URTI and OM, but was different for LRTI. Children who received the recommended 4 PCV doses had a lower LRTI and OM risk than unvaccinated children. There was a trend towards a lower OM risk associated with a mixed PCV10+ PCV13 schedule compared with PCV7. CONCLUSION: Results suggest a lower LRTI and OM risk associated with PCV use in this high-risk population but respiratory disease risk remains high compared with the general population in Quebec.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".