Influenza Burden in Febrile Infants and Young Children in a Pediatric Emergency Department
Bibliographic record
Abstract
BACKGROUND: In France, epidemiologic data in children in ambulatory settings are scarce. We aimed to measure the burden of influenza in young children. METHODS: Febrile children younger than 36 months were consecutively recruited in a pediatric emergency department during the 2002 epidemic peak. Virology analysis and follow-up were systematic. RESULTS: During calendar weeks 3 to 6, 2002, 575 children were recruited; 49% were positive: A/H3N2 in 44% and B in 5%. Prevalence rate was 57% in 12- to 35-month-old children and 39% in infants younger than 12 months. The main clinical pictures were nonrespiratory in one third of them. One of 8 patients had a complication. One of 10 patients was hospitalized, and the estimated specific hospitalization rate for the study period was 237 of 100,000 in the general population among infants younger than 12 months. Forty-two percent of children (n = 110) were prescribed antibiotics and at least 34% of them were inappropriate (n = 89). Median length of disease was 8 days, and 25% of the children had not fully recovered by day 11. Only one child had been previously vaccinated of 65 with chronic conditions. Both epidemic strains were covered by the vaccine. CONCLUSIONS: Health outcomes showed that influenza disease burden in young French children is similar to that observed in North America. An active vaccination strategy would have strongly reduced the burden of influenza and lowered antibiotic use. Continuous efforts are needed to reach requirements of our influenza vaccination policy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".