Gestational diabetes and neurodevelopmental disorders in offspring: A systematic review
Bibliographic record
Abstract
Introduction: Gestational diabetes (GDM) is one of the serious complications of pregnancy which can lead to adverse outcomes for mother and offspring in the short and long-term. This systematic review study was performed to investigate the relationship between GDM and neurodevelopmental disorders in the offspring. Methods: In this review study, to find the related articles, databases of PubMed, Scopus, Web of Science, and Google Scholar were searched using the keywords of gestational diabetes, offspring, cognitive disorder and behavioral disorder up to March 2022. The Newcastle-Ottawa scale was used to assess the quality of the studies. The findings of this study were reported qualitatively. Results: This study reviewed 14 observational studies with 728140 participants. The findings of included studies on the incident of neurodevelopmental complications in the offspring of mothers with GDM, who were diagnosed using different diagnostic criteria, show the possibility of neurodevelopmental complications in the offspring of mothers with GDM. The most common diagnosing criterion of GDM was the Carpenter-Coustan criterion; after that, the World Health Organization diagnostic criterion was the most common. Conclusion: The available evidence mainly indicated the association between GDM and neurodevelopmental disorders in the offspring, while due to limited studies, it was not possible to compare the outcomes of different diagnostic and therapeutic 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".