Safety and effectiveness of neuraminidase inhibitors in situations of pandemic and/or novel/variant influenza: a systematic review of the literature, 2009–15
Bibliographic record
Abstract
Objectives: To review systematically the published literature evaluating neuraminidase inhibitor (NI) safety and effectiveness in situations of pandemic and novel/variant influenza. Methods: We searched six online databases using comprehensive search criteria for observational studies and randomized controlled trials investigating the effects of NI treatment, prophylaxis or outbreak control in patients of all ages. Results: Overall, 165 studies were included (95% observational), which were generally of low methodological quality due to lack of adjustment for confounding variables. In studies reporting adjusted estimates in general populations, NI treatment appeared likely to be effective against mortality (primarily if administered within 48 h of symptom onset) and potentially effective in reducing pneumonia. NIs appeared effective in reducing secondary transmission when indicated for prophylaxis. Limited, low-quality data suggest NIs are likely safe in general populations and may be safe in pregnant women and children. Data are scarce regarding safety of NIs in adults and high-risk individuals. Conclusions: Most included studies were observational, statistically underpowered and at high risk of reporting biased and/or confounded effect estimates. NI treatment appeared likely effective in reducing mortality (cause unspecified) and pneumonia in general populations, with increasing benefit when administered with 48 h of symptom onset. NI pre- or post-exposure prophylaxis is likely effective in reducing secondary transmission of influenza in a general population. Our evidence suggests NIs are likely safe to use in the general population; however, data for children and pregnant women are limited. Knowledge gaps persist in specific populations such as Aboriginals, high-risk individuals and the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".