The risk of intellectual disability in offspring of diabetic mothers: A systematic review and meta-analysis
Bibliographic record
Abstract
Epidemiological evidence on association between maternal diabetes and intellectual disability (ID) in offspring is mixed. This systematic review and meta-analysis aimed to synthesise the existing evidence to determine the extent and nature of this association. We systematically searched Embase, Web of Science, Scopus, PubMed, PsycINFO, and CINAHL databases from inception to March 14, 2023. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Effect estimates for each exposure-outcome association were synthesised using a random-effects model Sensitivity and subgroup analyses were performed to identify potential sources of heterogeneity. A total of ten studies, comprising 8,927,706 mother-child pairs, met the inclusion criteria. Our analyses revealed that children exposed to any form of maternal diabetes had higher odds of ID compared to unexposed counterparts. Specifically, we found a 61 % higher risk of ID in offspring of mothers with any pre-existing diabetes. However, no significant association was found between gestational diabetes mellitus (GDM) and ID risk in offspring. The present meta-analysis suggests that exposure to pre-existing type 1 diabetes (T1D) and type 2 diabetes (T2D), but not GDM, is associated with increased risks of ID in offspring. Further high-quality studies, adequately adjusted for potential confounders, are needed to confirm these findings.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".