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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".