Bibliographic record
Abstract
Influenza viruses are major human pathogens with a global distribution, accounting for more than 500,000 annual deaths worldwide and with considerable impact on the quality of life and productivity of the society. Due to the limited efficacy of vaccination, antiviral drugs constitute a complementary approach in the control and prevention of influenza infections and thus play an important role in the management of influenza outbreaks and pandemics. Currently, adamantanes and neuraminidase inhibitors (NAIs) are the only two classes of anti-influenza agents approved for clinical use. However, the worldwide emergence and high prevalence of adamantane-resistant virus variants has discouraged the use of the former drugs. NAIs have proved to be very effective against influenza A and B viruses. Nevertheless, oseltamivir-resistant strains have also been reported quite frequently, as in the case of seasonal H1N1 viruses that circulated between 2007 and 2009. Indeed, the emergence of drug-resistant virus variants is always a matter of concern because it could significantly compromise the usefulness of such intervention. This highlights the need for continuous monitoring of resistance markers, as well as the development of new anti-influenza drugs and combination therapies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.011 | 0.007 |
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".