Updating the impact of mRNA COVID-19 vaccine exposure during pregnancy on obstetric and neonatal outcomes
Bibliographic record
Abstract
Being a new vaccine platform, continuous monitoring of the mRNA COVID-19 vaccines in pregnant women is of critical importance. This systematic review and meta-analysis evaluate the maternal and neonatal outcomes associated with mRNA COVID-19 vaccination during pregnancy. We conducted a systematic search of PubMed, Embase, Cochrane Library, and clinical trial registries for studies published between December 2020 and July 2024. Studies were included if they assessed obstetric and neonatal outcomes following mRNA COVID-19 vaccination in pregnant women. Data were extracted and analyzed using a random-effects model to calculate pooled odds ratios (ORs) and 95 % confidence intervals (CIs). Fifteen studies met the inclusion criteria, encompassing 42,944 vaccinated and 183,733 unvaccinated pregnant women. mRNA vaccination was associated with a significant reduction in preterm delivery (OR 0.743, 95 % CI 0.607-0.911), fetal distress (OR 0.699, 95 % CI 0.546-0.893), neonatal congenital abnormalities (OR 0.712, 95 % CI 0.570-0.889), and NICU admissions (OR 0.718, 95 % CI 0.617-0.836). However, a slight increase in gestational diabetes risk was observed (OR 1.107, 95 % CI 1.054-1.162). mRNA COVID-19 vaccines are safe during pregnancy and associated with reduced risks of adverse obstetric and neonatal outcomes. An observed marginal increase in gestational diabetes risk underscores the need for continuous monitoring. These findings support the inclusion of pregnant women in vaccination campaigns and inform public health policies and clinical practices to improve maternal and neonatal health outcomes.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".