Oseltamivir Reduces 30-Day Mortality in Older Adults With Influenza: A Pooled Analysis From the 2012–2019 Serious Outcomes Surveillance Network of the Canadian Immunization Research Network
Bibliographic record
Abstract
Background: Oseltamivir is recommended for the treatment of adults hospitalized with influenza, but adherence is often suboptimal. This may be due to doubts about the reliability of the evidence supporting its benefits, particularly when initiation is delayed. We aimed to assess the effectiveness of oseltamivir in reducing mortality in older adults hospitalized with influenza, with a focus on the timing of initiation. Methods: The CIRN-SOS Network gathered data on severe respiratory illnesses across 5 Canadian provinces during the influenza seasons 2012-2019. Individuals aged ≥65 years with confirmed influenza and available antiviral prescription data were included. We compared the 30-day survival rates of hospitalized patients based on oseltamivir prescription status. Kaplan-Meier estimated survival probability and inverse probability of treatment (IPT)-weighted Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality. The analyses considered the time to antiviral initiation (>48 vs ≤48 hours). Results: Among the 8135 influenza patients studied, 2126 did not receive antiviral treatment, whereas 6009 were treated with oseltamivir. A total of 395 patients were hospitalized for >30 days. The overall mortality rate was 8.32 per 1000 person-days, with 53.9% of the deaths occurring within the first week. Oseltamivir recipients had a 18% lower risk of 30-day mortality (IPT-weighted HR, 0.82 [95% CI, .69-.98]). The benefit was significant for influenza A (IPT-weighted HR, 0.74 [95% CI, .61-.91]) but not for influenza B (IPT-weighted HR, 1.12 [95% CI, .81-1.56]). Oseltamivir remained effective even when initiated after 48 hours (IPT-weighted HR, 0.66 [95% CI, .49-.90]). Influenza vaccination did not mediate the effectiveness of oseltamivir in reducing mortality. Conclusions: NCT01517191.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.023 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".