Perinatal Outcomes of Newborns of COVID-19-Infected Pregnant Women: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
In this systematic review and meta-analysis, we aimed to review the characteristics and outcomes of the newborns of Coronavirus disease 2019 (COVID-19) infected pregnant women. We conducted an online bibliographic search using the following electronic databases: MEDLINE via PubMed, Scopus, Web of Science, and Cochrane Central. Studies were deemed eligible if they recruited newborns from mothers with confirmed COVID-19 and reported the perinatal outcomes of neonatal COVID-19 cases. A total of 20 studies were included. Neonates born to mothers with positive COVID-19 results have been shown to have significantly lower birth weights (mean difference, MD = -48.54 g, p = 0.04), increased risks of fetal distress (odds ratio, OR = 1.76, p < 0.00001), respiratory distress (OR = 1.96, p = 0.006), premature birth (OR = 2.08, p < 0.00001), neonatal death (OR = 2.20, p = 0.004), and a lower 5-minute Apgar score (OR = 1.44, p = 0.02). Additionally, they were more likely to be admitted to the neonatal intensive care unit (NICU) (OR = 2.25, p = 0.007) and test positive for COVID-19 themselves (OR = 9.88, p = 0.03). However, other parameters, such as risks for malformations, mechanical ventilation, hypoglycemia, and sepsis, appeared to be comparable between the two groups. Maternal infection with COVID-19 during pregnancy is associated with several neonatal outcomes, some of which are adverse and others that do not show significant deviation from norms. While our meta-analysis clearly illustrates heightened risks associated with premature birth, reduced neonatal weight, and other challenges, it also emphasizes that not all neonatal outcomes can be directly attributed to maternal SARS-CoV-2 infection.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".