Maternal Infection During Pregnancy and Risk of Cerebral Palsy in Children: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and aim: The association between maternal infection during pregnancy and the risk of cerebral palsy has been previously reported. However, their results were relatively inconsistent. This systematic review and meta-analysis were carried out to investigate the association between maternal infection during pregnancy and the risk of cerebral palsy in children. Methods: PubMed, Scopus, and Web of Sciences databases were searched from inception to October 28, 2019. Heterogeneity was assessed using the I 2 value. In case of substantial heterogeneity (I 2 > 50%), a random effects model was applied, otherwise, a fixed effects model was used. The pooled associations were expressed as relative risks (RRs) and 95% confidence intervals (CIs). Publication bias and quality of studies included in the systematic review were checked using the Egger’s regression test and Newcastle-Ottawa Scale (NOS), respectively. Results: Thirty-seven studies were included in the systematic review. Among them, 21 studies were eligible for the meta-analysis. The pooled RR of cerebral palsy risk was 2.50 (95% CI 1.94, 3.21; I 2 = 88.7%, P < .001) among children born to mothers who had any infection during pregnancy. The risk was increased to 2.85 (95% CI 1.96, 4.15; I 2 = 75.9%, P < .001) when the mother was diagnosed with chorioamnionitis. Publication bias tests suggested no evidence of potential publication bias and 76% of the studies included in the meta-analysis were of high quality (NOS ≥ 6). Conclusion: This systematic review and meta-analysis provides evidence that maternal infection during pregnancy may be associated with an increased risk of cerebral palsy in children.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".