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Record W4406365250 · doi:10.1002/9781683670438.mcm0115

Mechanisms of Resistance to Antiviral Agents

2023· other· en· W4406365250 on OpenAlexaff
Robert W. Shafer, Guy Boivin, A Howe, Sunwen Chou

Bibliographic record

VenueClinMicroNow · 2023
Typeother
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsBC Centre for Disease ControlUniversité Laval
Fundersnot available
KeywordsResistance (ecology)VirologyBusinessBiologyComputer scienceEcology

Abstract

fetched live from OpenAlex

Abstract Understanding the mechanisms of viral drug resistance is critical for clinical management of individuals receiving antiviral therapy, for developing new antiviral drugs, and for drug resistance surveillance. This chapter reviews the mechanisms of resistance to antiviral drugs used to treat infections by eight common viruses: herpes simplex, cytomegalovirus, varicella‐zoster virus, human immunodeficiency virus type 1, influenza A and B, hepatitis B, hepatitis C, and severe acute respiratory syndrome coronavirus 2. Drug‐resistant virus subpopulations may exist at low levels in clinical isolates or may arise only during drug exposure. The error‐prone polymerases in RNA viruses render the development of resistance more frequent than in DNA viruses. Drug‐resistant viruses are identified by in vitro passage experiments in which wild‐type viruses are cultured in increasing concentrations of an inhibitory drug and by ex vivo analysis of virus isolates obtained from individuals receiving antiviral therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.052
GPT teacher head0.376
Teacher spread0.324 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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