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Record W2553620755 · doi:10.1080/21645515.2016.1251537

Review of seasonal influenza in Canada: Burden of disease and the cost-effectiveness of quadrivalent inactivated influenza vaccines

2016· review· en· W2553620755 on OpenAlexaffabout
Edward W. Thommes, Morgan Kruse, Michele Kohli, Rohita Sharma, Stephen G. Noorduyn

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2016
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsGlaxoSmithKline (Canada)University of Guelph
Fundersnot available
KeywordsVaccinationSeasonal influenzaImmunizationDisease burdenLive attenuated influenza vaccineMedicineEpidemiologyEnvironmental healthVirologyPublic healthInfluenza vaccineDiseaseImmunologyInfectious disease (medical specialty)PopulationCoronavirus disease 2019 (COVID-19)Internal medicineAntigen

Abstract

fetched live from OpenAlex

In the 2015/16 influenza season, the Canadian National Advisory Committee on Immunization (NACI) recommended vaccination with quadrivalent inactivated influenza vaccine (QIV) for infants aged 6-23 months and trivalent inactivated influenza vaccines (TIVs) or QIVs in adults. The objective of this review (GSK study identifier: HO-13-14054) is to examine the epidemiology and disease burden of influenza in Canada and the economic benefits of vaccination. To inform this review, we performed a systematic literature search of relevant Canadian literature and National surveillance data. Influenza B viruses from phylogenetically-distinct lineages (B/Yamagata and B/Victoria) co-circulate in Canada, and are an important cause of influenza complications. Modeling studies, including those postdating the search suggest that switching from TIV to QIV in Canada reduces the burden of influenza and would likely be cost-effective. However, more robust real-world outcomes data is required to inform health policy decision makers on appropriate influenza vaccination strategies for Canada.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.120
GPT teacher head0.421
Teacher spread0.301 · 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 designSystematic review
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

Citations41
Published2016
Admission routes2
Has abstractyes

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