Bibliographic record
Abstract
<div><p>Background</p><p>There is arguing evidence regarding the association between maternal infections during pregnancy and the risk of intellectual disability (ID) in children. This systematic review and meta-analysis are essential to determine and address inconsistent findings between maternal infections during pregnancy and the risk of ID in children.</p><p>Methods</p><p>The MOOSE and PRISMA guidelines were followed to perform and report on this study. The Medline/PubMed, Web of Science, Embase, and Scopus databases were searched from inception up to March 15, 2023, to identify potentially eligible studies. Inclusion and exclusion criteria were applied, as well as the Newcastle-Ottawa Scale was used to assess the methodological quality of studies included. The included studies were divided into two types based on the participants: (1) ID-based studies, which involved children with ID as cases and healthy children as controls and evaluated maternal infection in these participants; (2) infection-based studies, which assessed the prevalence or incidence of ID in the follow-up of children with or without exposure to maternal infection. We used Random-effects models (REM) to estimate the overall pooled odds ratio (OR) and 95% confidence intervals (CIs). The between-studies heterogeneity was assessed with the χ<sup>2</sup>-based Q-test and <i>I</i><sup>2</sup> statistic. Subgroup and sensitivity analyses were applied to explore the source of heterogeneity and results consistency.</p><p>Results</p><p>A total of eight studies including 1,375,662 participants (60,479 cases and 1,315,183 controls) met the eligibility criteria. The REM found that maternal infection significantly increased the risk of ID in children (OR, 1.33; 95% CI, 1.21–1.46; <i>I</i><sup>2</sup> = 64.6). Subgroup analysis showed a significant association for both infection-based (OR, 1.27; 95%CI, 1.15–1.40; <i>I</i><sup>2</sup> = 51.2) and ID-based (OR, 1.44; 95%CI, 1.19–1.74; <i>I</i><sup>2</sup> = 77.1) studies. Furthermore, subgroup analysis based on diagnostic criteria revealed a significant association when maternal infection or ID were diagnosed using ICD codes (OR, 1.33; 95% CI, 1.20–1.48; <i>I</i><sup>2</sup> = 75.8).</p><p>Conclusion</p><p>Our study suggests that maternal infection during pregnancy could be associated with an increased risk of ID in children. This finding is consistent across different types of studies and diagnostic criteria. However, due to the heterogeneity and limitations of the included studies, we recommend further longitudinal studies to confirm the causal relationship and the underlying mechanisms.</p></div>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.214 | 0.016 |
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; both teacher heads agree on what is shown here.
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".