Hospitalization Attributable to Influenza and Other Viral Respiratory Illnesses in Canadian Children
Bibliographic record
Abstract
BACKGROUND: We sought to estimate the incidence of hospitalization attributable to influenza virus infection in Canadian children while controlling for the impact of other respiratory viruses. METHODS: Hospital admissions for children and youth 0 to 19 years of age, 1994-2000, were modeled as a function of proxy variables for influenza, respiratory syncytial virus (RSV) and other respiratory viral activity, seasonality and trend, using a Poisson regression model with a linear link. These proxy variables were developed from influenza mortality and laboratory test results for influenza, RSV and other viruses. Various checks for consistency, model fit and robustness were conducted and guided model development. RESULTS: Overall, 1.5% of all pediatric respiratory admissions could be attributed to influenza (18 admissions per 100,000 per year). The largest burden was seen in infants 6 to 11 months of age with rates of 200 per 100,000 infants and approximately equivalent to the rate for adults aged 65 to 69. During peak influenza activity, 7% of respiratory admissions were attributable to influenza as were 35% of febrile seizure admissions. RSV and parainfluenza (PIV) were the major viral causes of hospital admission with rates of 130 and 160 per 100,000, respectively. Another 70 per 100,000 admissions were attributed to other influenza-like illnesses. CONCLUSIONS: Influenza is a significant cause of morbidity leading to hospitalization in Canadian children, particularly for those under 2 years of age. RSV, PIV and other respiratory viruses were found to be major causes of respiratory illness leading to hospital care, surpassing influenza.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".