TITLE: Antiviral Stockpiling Strategies for Pandemic Influenza: A Review of the Clinical and Cost-Effectiveness
Bibliographic record
Abstract
Influenza A viruses can intermittently cause worldwide pandemics with high rates of illness and death. A pandemic is possible at any time with the potential to cause serious illness, death, and substantial social and economic disruption globally. 1 While the characteristics such as the strain of influenza, clinical attack rate (CAR), fatality rate (FR), and hospitalization rate (HR), and duration of a future influenza pandemics are unpredictable, the World Health Organization (WHO) has recommended countries develop pandemic preparedness plans. 2 Part of a pandemic preparedness plan includes the stockpiling of antiviral (AV) agents. 3 The WHO has advised countries with adequate resources to stockpile AV drugs nationally for use at the start of a pandemic. 4 Vaccines could also provide protection against influenza viruses. However, antivirals will be the only specific medical intervention available during the initial pandemic response. 1 A vaccine will take at least four to six months to become available. 1 In Canada, M2 inhibitors (amantadine) and the neuraminidase inhibitors (oseltamivir and zanamivir) are the AV agents available for the prophylaxis and treatment of influenza. Both types of antiviral agents are 70 % to 90 % effective as prophylaxis and can shorten the duration of illness by 1.5 days when used in treatment. 5
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".