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Record W3015666557 · doi:10.1159/000504478

Influenza Vaccination: Accelerating the Process for New Vaccine Development in Older Adults

2020· review· en· W3015666557 on OpenAlexaff
Janet E. McElhaney, Melissa K. Andrew, Laura Haynes, George A. Kuchel, Shelly McNeil, Graham Pawelec

Bibliographic record

VenueInterdisciplinary topics in gerontology and geriatrics · 2020
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsDalhousie UniversityHealth Sciences North
Fundersnot available
KeywordsVaccinationProcess (computing)MedicineVirologyComputer science

Abstract

fetched live from OpenAlex

Increased susceptibility to the serious complications of influenza is common in older adults. It is often ascribed to weakening of the immune system with age, and 90% of influenza-related deaths occur in older adults despite widespread vaccination programs. Common chronic conditions not only contribute to the loss of immune protection after vaccination and increase the risk for serious outcomes of influenza, but also increase the long-term consequences following hospitalization. Interactions of T and B cell ageing, chronic elevation of inflammatory cytokines (sometimes dubbed "inflammaging"), and dysregulated acute cytokine production pose major challenges to the development of new and more effective vaccines. However, these age-related problems are modifiable, as we have shown, and provide a clear margin for improvement. This chapter describes how an exclusive focus on developing influenza vaccines to stimulate strain-specific antibody responses against the hemagglutinin surface glycoprotein of the influenza virus, to the exclusion of other potentially important mechanisms, is missing the mark in terms of preventing the serious complications of influenza in older adults. Novel approaches are needed to enhance antibody-mediated protection against infection and stimulate cell-mediated immune responses to clear influenza virus from the lungs. These strategies for improving vaccine effectiveness will address the public health need for "vaccine prevention of disability" to mitigate the global pressures of aging populations on health and social care systems.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.150
GPT teacher head0.465
Teacher spread0.315 · 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 teacher head, not a consensus.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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