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Burden of influenza B virus infection and considerations for clinical management

2020· review· en· W3095531782 on OpenAlexaff
Hassan Zaraket, Aeron C. Hurt, Barry Clinch, Ian Barr, Nelson Lee

Bibliographic record

VenueAntiviral Research · 2020
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Alberta
FundersDepartment of Health and Aged Care, Australian GovernmentRoche
KeywordsMedicineEpidemiologyPandemicNeuraminidaseDiseaseIntensive care medicineDisease burdenVirologyVirusClinical trialNeuraminidase inhibitorDisease managementImmunologyInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Internal medicine

Abstract

fetched live from OpenAlex

Influenza B viruses cause significant morbidity and mortality, particularly in children, but the awareness of their impact is often less than influenza A viruses partly due to their lack of pandemic potential. Here, we summarise the biology, epidemiology and disease burden of influenza B, and review existing data on available antivirals for its management. There has long been uncertainty surrounding the clinical efficacy of neuraminidase inhibitors (NAIs) for influenza B treatment. In this article, we bring together the existing data on NAIs and discuss these alongside recent large randomised controlled trial data for the new polymerase inhibitor baloxavir in high-risk influenza B patients. Finally, we offer considerations for the clinical management of influenza B, with a focus on children and high-risk patients where disease burden is highest.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.670
GPT teacher head0.635
Teacher spread0.035 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations95
Published2020
Admission routes1
Has abstractyes

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