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Record W4404473930 · doi:10.1080/21645515.2024.2421096

Seasonal influenza vaccines: Variability of immune responses to B lineage viruses

2024· review· en· W4404473930 on OpenAlexaff
Matthew S. Miller, Emanuele Montomoli, Eyal Leshem, Michael Schotsaert, Thomas Weinke, Nevena Vicic, Deborah Rudin

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2024
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersModerna
KeywordsImmunogenicityImmune systemVirologyBiologyImmunologyInfluenza vaccineSeasonal influenzaVirusImmunityInfluenza A virusDiseaseOriginal antigenic sinAntigenic driftMedicineInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Although influenza A viruses predominate globally, influenza B viruses are responsible for a significant and often underappreciated burden. Despite this, immunity to influenza B viruses remains understudied, and there is a perception that vaccine-mediated immune responses to influenza B strains are less robust than influenza A strains. This targeted literature review examines this concept using data from pivotal phase 3 immunogenicity studies on currently licensed seasonal influenza vaccines and explores several explanations for this phenomenon, including immune exposure history, assay limitations, virus-related properties inherent to B lineages, and strain mismatch. Overall, studies demonstrated vaccines induce variable and sometimes less robust immune responses to influenza B strains; however, further studies are needed to fully confirm and understand these observations. In identifying the potential causes of variable performance of current vaccines against influenza, this review aims to guide vaccine development to enhance overall vaccine performance and reduce disease burden worldwide.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.225
GPT teacher head0.485
Teacher spread0.260 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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