Antiviral Use in Canadian Children Hospitalized for Influenza
Bibliographic record
Abstract
OBJECTIVES: Antivirals are recommended for children hospitalized with influenza but are underutilized. We describe antiviral prescribing during influenza admissions in Canadian pediatric centers and identify factors associated with antiviral use. 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 2010-2011 to 2018-2019. Logistic regression analyses were used to identify factors associated with antiviral use. RESULTS: < .001). On multivariable analysis, factors associated with antiviral use included older age (adjusted odds ratio [aOR] 1.04 [95% confidence interval (CI), 1.02-1.05]), more recent season (highest aOR 9.18 [95% CI, 6.70-12.57] for 2018-2019), admission during peak influenza period (aOR 1.37 [95% CI, 1.19-1.58]), availability of local treatment guideline (aOR 1.54 [95% CI, 1.17-2.02]), timing of laboratory confirmation (highest aOR 2.67 [95% CI, 1.97-3.61] for result available before admission), presence of chronic health conditions (highest aOR 4.81 [95% CI, 3.61-6.40] for cancer), radiographically confirmed pneumonia (aOR 1.39 [95% CI, 1.20-1.60]), antibiotic treatment (aOR 1.51 [95% CI, 1.30-1.76]), respiratory support (1.57 [95% CI, 1.19-2.08]), and ICU admission (aOR 3.62 [95% CI, 2.88-4.56]). CONCLUSIONS: Influenza antiviral agents were underused in Canadian pediatric hospitals, including among children with high-risk chronic health conditions. Prescribing varied considerably across sites, increased over time, and was associated with patient and hospital-level characteristics. Multifaceted hospital-based interventions are warranted to strengthen adherence to influenza treatment guidelines and antimicrobial stewardship practices.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".