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Record W3027578074 · doi:10.3389/fimmu.2020.01282

Global Perspectives on Immunization During Pregnancy and Priorities for Future Research and Development: An International Consensus Statement

2020· review· en· W3027578074 on OpenAlexaff
Bahaa Abu-Raya, Kirsten Maertens, Kathryn M. Edwards, Saad B. Omer, Janet A. Englund, Katie L. Flanagan, Matthew D. Snape, Gayatri Amirthalingam, Elke Leuridan, Pierre Van Damme, Vana Papaevangelou, Odile Launay, Ron Dagan, Magda Campins, Anna Franca Cavaliere, T. Frusca, Sofia Guidi, Miguel O’Ryan, Ulrich Heininger, Tina Q. Tan, Ahmed R. Alsuwaidi, Marco Aurélio Palazzi Sáfadi, Luz María Vilca, Nasamon Wanlapakorn, Shabir A. Madhi, Michelle Giles, Roman Prymula, Shamez Ladhani, Federico Martinón‐Torres, Litjen Tan, Lessandra Michelin, Giovanni Scambia, Nicola Principi, Susanna Esposito

Bibliographic record

VenueFrontiers in Immunology · 2020
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineImmunizationPregnancyVaccinationIntensive care medicinePandemicTetanusConsensus conferenceDiseaseFamily medicineImmunologyInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Antibody

Abstract

fetched live from OpenAlex

Immunization during pregnancy has been recommended in an increasing number of countries. The aim of this strategy is to protect pregnant women and infants from severe infectious disease, morbidity and mortality and is currently limited to tetanus, inactivated influenza, and pertussis-containing vaccines. There have been recent advancements in the development of vaccines designed primarily for use in pregnant women (respiratory syncytial virus and group B Streptococcus vaccines). Although there is increasing evidence to support vaccination in pregnancy, important gaps in knowledge still exist and need to be addressed by future studies. This collaborative consensus paper provides a review of the current literature on immunization during pregnancy and highlights the gaps in knowledge and a consensus of priorities for future research initiatives, in order to optimize protection for both the mother and the infant.

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)0.014
metaresearch head score (Gemma)0.010
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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.002

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 head0.096
GPT teacher head0.435
Teacher spread0.338 · 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

Citations128
Published2020
Admission routes1
Has abstractyes

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