Neurodevelopmental Outcomes in Children Born to Mothers Infected with SARS-CoV-2 During Pregnancy: A Narrative Review
Bibliographic record
Abstract
Background: The potential impact of maternal SARS-CoV-2 infection during pregnancy on the neurodevelopment of offspring has raised considerable concern. Emerging studies have evaluated various developmental domains in exposed infants, yet findings remain inconsistent. Objective: To synthesize current evidence regarding neurodevelopmental outcomes in infants born to mothers with confirmed SARS-CoV-2 infection during pregnancy. Methods: We conducted a narrative review following PRISMA guidelines. A literature search was performed in PubMed, Cochrane, and ScienceDirect using keywords including “COVID-19”, “pregnancy”, “neurodevelopment”, and “SARS-CoV-2”. Nineteen studies were included. Data were extracted regarding study design, sample size, timing of exposure, age at assessment, developmental tools used, and key findings. Study quality was assessed using the Newcastle–Ottawa Scale. Results: Among 19 included studies, 12 reported at least some neurodevelopmental delays, particularly in motor and language domains. However, these delays were generally mild, domain-specific, and often not statistically significant. Seven studies, most of which were high-quality and low-risk, reported no significant differences between exposed and unexposed groups. Assessment tools and follow-up durations varied widely, limiting comparability. Conclusions: Current evidence does not support a consistent association between in utero SARS-CoV-2 exposure and an unfavorable neurodevelopmental outcome up to 24 months. However, heterogeneity in methods and short-term follow-up warrant further high-quality longitudinal research.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".