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Record W4406899527 · doi:10.1159/000543690

Interventions to Prevent and Manage Infections in Pregnancy

2025· review· en· W4406899527 on OpenAlexaff
Rahima Yasin, Li Jiang, Jai K Das, Zulfiqar A Bhutta

Bibliographic record

VenueNeonatology · 2025
Typereview
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsHospital for Sick Children
FundersBill and Melinda Gates Foundation
KeywordsMedicinePregnancyPsychological interventionLow birth weightRegimenPediatricsObstetricsIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Care interventions aimed at preventing and treating maternal infections during the gestational period are of paramount importance. Timely immunizations, screening strategies, and management of maternal infections reduce the risk of complications for the developing fetus and play a pivotal role in improving neonatal outcomes. SUMMARY: We aim to summarize evidence for a total of thirteen interventions, pertaining to the prevention and treatment of maternal infections during the antenatal period, from Every Newborn Series published in The Lancet 2014. We identified the most recent systematic reviews, extracted data from each review, and conducted a sub-group meta-analysis for low-income countries and lower-middle-income countries (LMICs) for outcomes relevant to neonatal health. Findings from our papers indicate limited evidence from LMICs, highlighting the pressing need for coordinated efforts to close this gap and strengthen the body of inclusive evidence on prevention and treatment of maternal infections during pregnancy. KEY MESSAGES: Evidence from LMICs suggests that influenza virus vaccination had no effect on stillbirth, preterm birth, small for gestational age, or low birthweight (LBW). Insecticide-treated bed nets in pregnancy reduced the risk of fetal loss and improved the babies' birthweight. Changing a two-dose intermittent preventive treatment (IPTp) regimen to more frequent IPTp dosing decreased the risk of LBW and significantly improved babies' birthweight. Addition of antibacterial antibiotic to the IPTp regimen significantly reduced the risk of LBW. Antibiotic treatments for syphilis and chlamydia had a significant effect on LBW. Treatment of documented periodontal disease during pregnancy reduced the risk of LBW. BACKGROUND: Care interventions aimed at preventing and treating maternal infections during the gestational period are of paramount importance. Timely immunizations, screening strategies, and management of maternal infections reduce the risk of complications for the developing fetus and play a pivotal role in improving neonatal outcomes. SUMMARY: We aim to summarize evidence for a total of thirteen interventions, pertaining to the prevention and treatment of maternal infections during the antenatal period, from Every Newborn Series published in The Lancet 2014. We identified the most recent systematic reviews, extracted data from each review, and conducted a sub-group meta-analysis for low-income countries and lower-middle-income countries (LMICs) for outcomes relevant to neonatal health. Findings from our papers indicate limited evidence from LMICs, highlighting the pressing need for coordinated efforts to close this gap and strengthen the body of inclusive evidence on prevention and treatment of maternal infections during pregnancy. KEY MESSAGES: Evidence from LMICs suggests that influenza virus vaccination had no effect on stillbirth, preterm birth, small for gestational age, or low birthweight (LBW). Insecticide-treated bed nets in pregnancy reduced the risk of fetal loss and improved the babies' birthweight. Changing a two-dose intermittent preventive treatment (IPTp) regimen to more frequent IPTp dosing decreased the risk of LBW and significantly improved babies' birthweight. Addition of antibacterial antibiotic to the IPTp regimen significantly reduced the risk of LBW. Antibiotic treatments for syphilis and chlamydia had a significant effect on LBW. Treatment of documented periodontal disease during pregnancy reduced the risk of LBW.

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)0.000
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 score0.924
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.391
Teacher spread0.355 · 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 designOther design
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

Citations3
Published2025
Admission routes1
Has abstractyes

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