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Record W3117152868 · doi:10.15173/mumj.v17i1.2347

Universal Vaccines against Influenza Viruses: Overview of the Past, Present, and Prospective

2020· article· en· W3117152868 on OpenAlexaff
Yonathan Agung, Hannah D. Stacey, Michael R. D’Agostino, Ali Zhang

Bibliographic record

VenueMcMaster University Medical Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSore throatMedicineVirusHeadachesVaccinationVirologyTransmission (telecommunications)Intensive care medicineRespiratory tract infectionsImmunologyRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

Influenza is a common disease caused by influenza virus infections. There are an estimated 3 to 5 million annual cases of severe illness and 290 000 to 650 000 respiratory deaths caused by influenza virus infections worldwide (1). Transmission can occur in three ways: direct contact with an infected person, through fomites, or by inhaling aerosolized infectious particles (2). Systemic manifestations of uncomplicated influenza include fever, fatigue, and headaches, and symptoms of upper respiratory tract infection including sore throat, nasal discharge, and non-productive cough (3). Although antiviral drugs are available to treat influenza, vaccination remains the best way to prevent infection. This article will provide an overview of influenza virus biology, as well as current methods and those in development to treat and prevent influenza.

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)0.002
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.102
GPT teacher head0.339
Teacher spread0.236 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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