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Record W2736171204 · doi:10.1080/21645515.2017.1343226

Influenza vaccination in the elderly

2017· review· en· W2736171204 on OpenAlexfundno aff
Roman Chlíbek, Jana Shaw, Miroslav Špliňo, Roman Prymula

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2017
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
FundersEuropean Centre for Disease Prevention and ControlPublic Health Agency of Canada
KeywordsImmunosenescenceVaccinationMedicineImmunologyPublic healthInfluenza vaccineImmunizationVaccine efficacyPopulationDemographicsEnvironmental healthIntensive care medicineImmune systemDemography

Abstract

fetched live from OpenAlex

Seasonal influenza is a prevalent and serious annual illness resulting in widespread morbidity and economic disruption throughout the population; the elderly and immunocompromised are particularly vulnerable to serious sequelae and mortality. The changing demographics worldwide to an aging society have important implications for public health policy and pharmaceutical innovations. For instance, primary prevention via immunization is effective in reducing the burden of influenza illness among the elderly. However, the elderly may be insufficiently protected by vaccination due to the immunosenescence which accompanies aging. In addition, vaccine hesitancy among the younger populations increases the likelihood of circulating infectious diseases, and thus concomitant exposure. While it is clear that the development of more immunogenic vaccines is an imperative and worthy endeavor, clinical trials continue to demonstrate that the current influenza vaccine formulation remains highly effective in reducing morbidity and mortality when well matched to circulating strains.

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)0.002
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0010.001
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 head0.420
GPT teacher head0.530
Teacher spread0.110 · 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

Citations165
Published2017
Admission routes1
Has abstractyes

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