Maternal Infection during Pregnancy and Attention-Deficit Hyperactivity Disorder in Children: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: We aimed to determine the association between maternal infections during pregnancy with risk of Attention-Deficit Hyperactivity Disorder (ADHD) in children. Methods: A systematic literature search was performed utilizing the online databases PubMed, Scopus, and Web of Sciences up to July 2020. Random-effects meta-analyses were applied to estimate pooled relative risk (RR). Heterogeneity, study quality and publication bias were assessed through I2 value, Newcastle–Ottawa scale (NOS) and Egger's test, respectively. Results: Thirteen articles involving 1401904 mother-child pairs were included. The result of meta-analysis showed that the risk of ADHD increased by 30% among children whose mothers took any infections during pregnancy (pooled RR=1.30, 95% CI: 1.14-1.49; I2=85.5, P<0.001). Overall, the included studies were good in quality and no publication bias was found (P=0.23, Egger’s test). Conclusion: Maternal infections during pregnancy might be associated with an increased risk of ADHD in children.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".