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Record W3114352800 · doi:10.1542/neo.22-1-e25

Protection of the Newborn Through Vaccination in Pregnancy

2021· review· en· W3114352800 on OpenAlex
Bahaa Abu-Raya, Kirsten Maertens

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeoReviews · 2021
Typereview
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineVaccinationPregnancyPediatricsTetanusBordetella pertussisImmunology

Abstract

fetched live from OpenAlex

Newborns and infants are at risk for severe infections with some pathogens (eg, Bordetella pertussis, influenza, respiratory syncytial virus, group B Streptococcus) during early life. To decrease this window of high susceptibility to some infections during early life and protect young infants, vaccination in pregnancy against some vaccine-preventable diseases (eg, influenza, pertussis, tetanus) has been recommended in an increasing number of countries with notable success. In addition, recent advances have been made in developing vaccines for pregnant women with the aim of reducing the respiratory syncytial virus and group B Streptococcus burden in infancy. In this article, we review the vaccines currently recommended during pregnancy and their benefits to newborns and infants. We also discuss progress made in the development of other vaccines that are expected to be evaluated in pregnant women in the near future.

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.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.124
GPT teacher head0.392
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