Interventions to Prevent and Manage Infections in Pregnancy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".