Exploring the effects of maternal anemia on neonatal neurodevelopment: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Maternal anemia, particularly iron deficiency anemia (IDA), affects about 30% of women globally. Iron is vital for fetal brain development, influencing myelination and oxygen transport. The extent of maternal anemia’s effect on neonatal neurodevelopment remains unclear. This meta-analysis examines how maternal anemia affects neonatal cognitive, motor, and socioemotional outcomes.Methods A systematic review and meta-analysis were conducted using PubMed, Scopus, and Web of Science, including studies published until December 2024. After screening 1,388 articles, six studies met the inclusion criteria (sample sizes: 178–636). Eligible studies examined maternal iron levels and neonatal neurodevelopment, assessed using standardized tools. Pooled effect sizes were calculated, and study quality was evaluated using the Newcastle-Ottawa Scale.Results No significant association was found between maternal anemia and neonatal habituation (p = 0.93), orientation (p = 0.76), state regulation (p = 0.90), motor maturity (p = 0.71), autonomic stability (p = 0.10), early learning composite (p = 0.65), or gross motor function (p = 0.59). Heterogeneity was low to moderate, with no publication bias detected.Conclusion Maternal anemia showed no significant short-term effect on neonatal habituation, orientation, motor maturity, or gross motor function. Future studies should assess long-term neurodevelopment to clarify potential delayed effects and refine treatment approaches.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".