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Record W7057551207

Immunosenescence: implications for vaccination programs in the elderly

2015· review· en· W7057551207 on OpenAlexaboutno aff

Bibliographic record

VenueDove Medical Press (Taylor and Francis Group) · 2015
Typereview
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationImmunogenicityDiseaseImmune systemInfectious disease (medical specialty)Immunological memoryImmunosenescenceImmunity
DOInot available

Abstract

fetched live from OpenAlex

Dessi Loukov,1,2,* Avee Naidoo,1,2,* Dawn ME Bowdish1,21Department of Pathology and Molecular Medicine, McMaster Immunology Research Centre, McMaster University, Hamilton, ON, Canada; 2Department of Pathology and Molecular Medicine, Institute for Infectious Diseases Research, McMaster University, Hamilton, ON, Canada *These authors contributed equally to this work Abstract: Worldwide, infectious disease is responsible for much of the morbidity and mortality in the elderly. As the number of individuals over the age of 65 increases, the economic and social costs of treating these infections will become a major challenge. Vaccination is the most effective and least costly preventative measure in our arsenal; however, vaccines that are effective in children and young adults are often ineffective in older adults. This is a result of the deterioration in immune function that occurs with age, referred to as immunosenescence. Age-associated changes in leukocyte phenotype and function impair primary vaccine responses and weaken long-lasting memory responses. In this review, we discuss current vaccination approaches in the elderly and strategies to improve responsiveness in older adults, which include increasing vaccine immunogenicity and overcoming the fundamental immune defects that prevent optimal immune responses. Keywords: immunosenescence, vaccination, elderly, influenza, pneumonia, zoster

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.083
GPT teacher head0.371
Teacher spread0.287 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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