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Record W2108628105 · doi:10.1093/jac/dkq100

Targeting pandemic influenza: a primer on influenza antivirals and drug resistance

2010· review· en· W2108628105 on OpenAlexaff
Ronald B. Moss, Richard T. Davey, Roy T. Steigbigel, Fang Fang

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2010
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsPandemicVirologyDrug resistanceInfluenza A virusVirusPopulationInfluenza pandemicBiologyDrug developmentDrugMedicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)MicrobiologyDiseaseEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

The emergence of the 2009 H1N1 pandemic influenza A virus, as well as constant antigenic drift of seasonal influenza, underscores the remarkable versatility of this virus in adapting to the human population. While vaccines are the principal public health defence against influenza, rapid vaccine development can be a daunting task. Antiviral drugs offer the promise of inhibiting influenza regardless of its genetic variations. However, the rapid rise of resistance to several antivirals has highlighted the need for developing novel therapeutics with reduced drug resistance potential. In this review, we will summarize the effects of the currently licensed anti-influenza drugs as well as the candidates in development against the seasonal and the 2009 H1N1 pandemic influenza A virus with an emphasis on drug resistance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.411
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations74
Published2010
Admission routes1
Has abstractyes

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