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Burden of influenza B virus infection and considerations for clinical management

2020· review· en· W3095531782 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.978
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.670
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