Influenza vaccination options to prevent hospitalization
Bibliographic record
Abstract
BACKGROUND: Vaccination of children against influenza remains a controversial topic despite the substantial morbidity caused by this infection. OBJECTIVE: To estimate the effect of three different vaccination strategies on preventing hospitalization due to influenza. METHODS: A retrospective chart review was conducted of all children admitted to a tertiary health care centre who tested positive for influenza during three consecutive influenza seasons. RESULTS: The final analysis included 208 cases with an age range of five days to 16.1 years. Seventy-six children were considered 'high-risk' and 132 were considered 'previously healthy'. Length of stay (LOS) ranged from one day to 46 days with a mean of 6.3 days. The mean LOS was 8.6 days for children with risk factors and 4.9 days for those without risk factors. The number of preventable influenza admissions was determined over three years and averaged over one year for the three vaccination strategies. A universal strategy of vaccinating all previously healthy and high-risk children over six months of age would have prevented 118 admissions. Using a selective strategy of vaccinating only children over six months of age with risk factors and a third strategy of vaccinating only two- to six-month-old infants would have prevented 58 and 55 admissions, respectively. CLINICAL IMPLICATION: Use of the universal vaccination strategy would have prevented over one-half of the influenza admissions, which was over twice that of targeted vaccination. Until the challenges of implementing universal vaccination are fully understood, targeted vaccination remains an acceptable alternative.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".