A scoping review of existing guidelines and recommendations for the use of influenza antiviral medications
Bibliographic record
Abstract
Background: Antiviral medications (AV) are available to mitigate mortality, morbidity, and absenteeism associated with influenza. Management of the burden of influenza is a concern to governing organizations, and thus it is important to understand how AVs are used in practice. To address this, the study reviewed recommendations and guidelines for AV use in situations of seasonal, pandemic, and novel/variant influenza from organizations around the world. Methods: Electronic databases, government and international websites, and Google were searched for guidelines and recommendations developed at the national and international levels by governments, intergovernmental organizations, and task forces that identify AV use recommendations. Results: Of 609 documents retrieved from the electronic search and manual review, 57 were included. Neuraminidase inhibitors (NIs) were recommended for use in nearly 80% of guidelines. Oseltamivir or oseltamivir/zanamivir were recommended for use in 38.6% and 40.4% of guidelines, respectively. Most guidelines based their recommendations on explicit evidence, the majority of which cited WHO documents. AV use was recommended for the general population in 42 guidelines; oseltamivir was recommended most commonly for both prophylaxis and treatment. Conclusions: The majority of guidelines covering subpopulations recommended the use of AVs. Details of AV administration (dose, duration, and timing), when reported, were consistent by indication. Guidelines recommending use of adamantanes were either published before 2007 or recommended their use for specific subpopulations. Guidelines were generally consistent in recommending the use of NIs for indication, type of influenza, setting, subpopulation, and evidence used to inform the recommendation.
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 | 0.035 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".