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Record W3128046729 · doi:10.1016/j.vaccine.2020.09.005

Introductory paper: High-dose influenza vaccine

2021· editorial· en· W3128046729 on OpenAlexaff
Mia Diaco, Lee-Jah Chang, Bruce T. Seet, Corey Robertson, Ayman Chit, Monica Mercer, David P. Greenberg, Rosalind Hollingsworth, Sandrine Samson

Bibliographic record

VenueVaccine · 2021
Typeeditorial
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsSanofi (Canada)
Fundersnot available
KeywordsInfluenza vaccineLive attenuated influenza vaccineMedicineSeasonal influenzaPublic healthVirologyVaccinationEnvironmental healthImmunologyFamily medicineCoronavirus disease 2019 (COVID-19)Internal medicineInfectious disease (medical specialty)DiseaseNursing

Abstract

fetched live from OpenAlex

Seasonal influenza has a significant impact on global public health each year, especially in older adults 65 years of age and above. This paper presents the evolution of high-dose influenza vaccine and the quantity as well as quality of evidence on this vaccine. Its introduces other peer-reviewed manuscripts included in this supplement covering the benefits high-dose influenza vaccine over ten consecutive influenza seasons. The development of the high-dose influenza vaccine represents an important step in the evolution of influenza vaccines, offering an advancement in prevention of influenza and a step in encouraging healthy aging in older adults. A video summary of the article can be accessed via the Supplementary data link at the end of this article.

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)0.003
metaresearch head score (Gemma)0.016
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0340.018

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 head0.028
GPT teacher head0.349
Teacher spread0.321 · 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
GenreEditorial

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

Citations15
Published2021
Admission routes1
Has abstractyes

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