Safety of psychotropic medications in pregnancy: an umbrella review
Bibliographic record
Abstract
Weighing risks and benefits of the use of psychotropic medications during pregnancy remains a challenge worldwide. We systematically assessed the strength of associations between psychotropic medication use in pregnant people with mental disorders and various adverse health outcomes in both pregnant people and foetuses. Systematic reviews with meta-analyses of observational studies investigating the association between exposure to psychotropic medication in pregnancy and any adverse health outcomes were included. Credibility was graded into convincing, highly suggestive, suggestive, weak or not significant. Quality of the meta-analyses and of individual studies were assessed with A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) the Newcastle-Ottawa Scale (NOS), respectively. We considered 21 meta-analyses encompassing 17,290,755 participants (AMSTAR 2 high = 1, low = 12, or critically low = 8). Evidence was suggestive for: (1) preterm birth in pregnant people with either any mental disorder (equivalent odds ratio 1.62 (95% confidence interval 1.24-2.12) or depression (1.65 [1.34-2.02]) receiving antidepressants during any trimester of pregnancy; (2) small for gestational age for pregnant people with depression receiving a SSRI during any trimester of pregnancy (1.50 [1.19-1.90]); and (3) major congenital malformation (1.24 [1.09-1.40]) or cardiac malformations (1.28 [1.11-1.47]) in babies for pregnant people with depression or anxiety receiving paroxetine during first trimester of pregnancy. Additional associations were supported by weak evidence, or were not statistically significant. This umbrella review found no convincing or highly suggestive level of evidence of adverse health outcomes associated with psychotropic medication use in pregnant people with mental disorders.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".