Bloodstream Infections in Children Hospitalized for Influenza, the Canadian Immunization Monitoring Program Active
Bibliographic record
Abstract
BACKGROUND: We aimed to estimate the proportion of children hospitalized for influenza whose illness was complicated by bloodstream infection, describe their clinical course, and identify the factors associated with bloodstream infection. METHODS: We performed active surveillance for laboratory-confirmed influenza hospitalizations among children ≤16 years old at the 12 Canadian Immunization Monitoring Program Active hospitals, from the 2010-2011 to 2020-2021 influenza seasons. Factors associated with bloodstream infection were identified using multivariable logistic regression analyses. RESULTS: Among 9179 laboratory-confirmed influenza hospital admissions, bloodstream infection occurred in 87 children (0.9%). Streptococcus pyogenes (22%), Staphylococcus aureus (18%) and Streptococcus pneumoniae (17%) were the most common bloodstream infection pathogens identified. Children with cancer [adjusted odds ratio (aOR): 2.78; 95% confidence interval (CI): 1.23-5.63], a laboratory-confirmed nonbloodstream bacterial infection (aOR: 14.1; 95% CI: 8.04-24.3) or radiographically-confirmed pneumonia (aOR: 1.87; 95% CI: 1.17-2.97) were more likely to experience a bloodstream infection, whereas children with chronic lung disorders were less likely (aOR: 0.41; 95% CI: 0.19-0.80). Disease severity markers such as intensive care unit admission (aOR: 2.11; 95% CI: 1.27-3.46), mechanical ventilation (aOR: 2.84; 95% CI: 1.63-4.80) and longer hospital length of stay (aOR: 1.02; 95% CI: 1.01-1.03) were associated with bloodstream infection. Bloodstream infection also increased the odds of death (aOR: 13.0; 95% CI: 4.84-29.1) after adjustment for age, influenza virus type and the presence of any at-risk chronic condition. CONCLUSIONS: Bloodstream infections, although infrequent, are associated with intensive care unit admission, mechanical ventilation, increased hospital length of stay and in-hospital mortality, thus requiring increased levels of care among pediatric influenza hospitalizations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".