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Record W4396541867 · doi:10.1126/scitranslmed.adm9183

Vaccination for healthy aging

2024· review· en· W4396541867 on OpenAlexafffund
David E. Bloom, Simone Pecetta, Francesco Berlanda Scorza, Andrea Carfı́, Bruce Carleton, Mariateresa Cipriano, Kathryn M. Edwards, Gianmarco Gasperini, Richard Malley, Arindam Nandi, Aurélia Nguyen, Lynda M. Stuart, Steve Black, Rino Rappuoli

Bibliographic record

VenueScience Translational Medicine · 2024
Typereview
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of British Columbia
FundersHealth CanadaSeqirusProvincial Health Services AuthorityCenters for Disease Control and PreventionBavarian NordicGenome British ColumbiaModernaOpen Philanthropy ProjectNational Institutes of HealthNovavaxGlaxoSmithKlineChildren's Hospital FoundationWellcome TrustBC Children's HospitalBill and Melinda Gates FoundationPfizerPan American Health OrganizationGenome CanadaSanofi
KeywordsVaccinationHealthy agingMedicineVirologyImmunologyGerontology

Abstract

fetched live from OpenAlex

As the world's population grows older, vaccination is becoming a key strategy for promoting healthy aging. Despite scientific progress in adult vaccine development, obstacles such as immunosenescence and vaccine hesitancy remain. To unlock the potential of adult vaccines fully, we must enhance immunization programs, dispel misinformation, and invest in research that deepens our understanding of aging and immunity.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.981
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.135
GPT teacher head0.493
Teacher spread0.358 · 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.

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

Citations13
Published2024
Admission routes2
Has abstractyes

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