Antidepressant use during pregnancy and gestational diabetes: a systematic review
Bibliographic record
Abstract
A high number of women are exposed to antidepressants during pregnancy. Considering that an association between exposure to antidepressants and type 2 diabetes was found in the general population and pregnant women are inherently susceptible to insulin resistance, this study aimed to investigate data in the scientific literature on the association between antidepressant use during pregnancy and the risk of developing gestational diabetes mellitus (GDM). This systematic review was conducted according to the PRISMA guidelines. PubMed, Virtual Health Library (VHS), and Web of Science databases were searched to identify observational studies reporting the association between antidepressant use during pregnancy and GDM. Review articles, case reports, case series, clinical trials, and animal studies were excluded. In total, 67 studies were retrieved, of which 3 were included in the systematic review: one case-control and two cohort studies. According to the Newcastle-Ottawa Scale, the three studies were considered high-quality. Through this systematic review, selective serotonin reuptake inhibitors (SSRI) use during pregnancy is not significantly associated with a higher risk of developing GDM. There are still controversies about the association between serotonin and norepinephrine reuptake inhibitors (SNRI) and GDM. The use of tricyclic, tetracyclic, and atypical antidepressants by pregnant women appears to be associated with GDM. Therefore, the available information about the topic is scarce and the condition of further studies is needed.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".