Antiviral Drugs for Post-Exposure Prophylaxis Against Influenza A or Influenza B
Bibliographic record
Abstract
What Is the Issue? Antiviral medications for influenza can be used after exposure but before symptom onset to reduce the occurrence of symptomatic and asymptomatic influenza. There are several different antiviral medications available for preventing influenza after exposure (also known as post-exposure prophylaxis). It is important to understand their overall and comparative efficacy for treatment and stockpiling decisions. What Did We Do? We conducted a rapid review of the recent evidence on the clinical efficacy and safety of different antiviral medications available in Canada for post-exposure prophylaxis. We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2020. What Did We Find? We identified 2 systematic reviews (SRs) of 12 randomized studies published in 2024. The included studies examined the efficacy of 3 antiviral medications (baloxavir, oseltamivir, and zanamivir) in preventing influenza infection. The reviews were generally well conducted. The findings show that these 3 antiviral medications are effective in reducing symptomatic influenza infection; however, the evidence on their impact on overall influenza and asymptomatic influenza infections is less clear. The evidence on the impact of these medicines on hospitalization and mortality was generally weak, with no strong conclusions. There is also little evidence of any additional adverse events (side effects) from these medicines. We found no evidence on peramivir for any of the outcomes. What Does It Mean? The included studies support the efficacy and safety of 3 antiviral medications in post-exposure settings for influenza prevention. More evidence is needed to understand their impact on hospitalization and mortality.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".