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Record W2750945775 · doi:10.1016/j.vaccine.2017.08.037

Influenza epidemiology and immunization during pregnancy: Final report of a World Health Organization working group

2017· article· en· W2750945775 on OpenAlexafffund
Deshayne B. Fell, Eduardo Azziz‐Baumgartner, Michael G. Baker, Maneesh Batra, Julien Beauté, Philippe Beutels, Niranjan Bhat, Zulfiqar A Bhutta, Cheryl Cohen, Bremen De Mucio, Bradford D. Gessner, Michael G. Gravett, Mark A. Katz, Marian Knight, Vernon J. Lee, Mark Loeb, Michiel Luteijn, Helen Marshall, Harish Nair, Kevin Pottie, Rehana A Salam, David A. Savitz, Suzanne Jacob Serruya, Becky Skidmore, Justin R. Ortiz

Bibliographic record

VenueVaccine · 2017
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster UniversitySickKids FoundationCentre for Global Health ResearchChildren's Hospital of Eastern OntarioHospital for Sick ChildrenUniversity of Ottawa
FundersCenters for Disease Control and PreventionMonash UniversityOttawa Hospital Research InstituteMcGill UniversityUniversity of TorontoWorld Health Organization
KeywordsMedicineImmunizationObservational studyEpidemiologyInfluenza vaccinePregnancyVaccine efficacyEnvironmental healthVaccinationPublic healthDisease burdenDiseasePediatricsIncidence (geometry)Family medicineImmunologyPopulationInternal medicineNursing

Abstract

fetched live from OpenAlex

From 2014 to 2017, the World Health Organization convened a working group to evaluate influenza disease burden and vaccine efficacy to inform estimates of maternal influenza immunization program impact. The group evaluated existing systematic reviews and relevant primary studies, and conducted four new systematic reviews. There was strong evidence that maternal influenza immunization prevented influenza illness in pregnant women and their infants, although data on severe illness prevention were lacking. The limited number of studies reporting influenza incidence in pregnant women and infants under six months had highly variable estimates and underrepresented low- and middle-income countries. The evidence that maternal influenza immunization reduces the risk of adverse birth outcomes was conflicting, and many observational studies were subject to substantial bias. The lack of scientific clarity regarding disease burden or magnitude of vaccine efficacy against severe illness poses challenges for robust estimation of the potential impact of maternal influenza immunization programs.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.150
GPT teacher head0.417
Teacher spread0.268 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations98
Published2017
Admission routes2
Has abstractyes

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