Pregnancy Outcomes Following In Utero Exposure to Second-Generation Antipsychotics
Bibliographic record
Abstract
Second-generation antipsychotics (SGAs) are increasingly used for a variety of mental illnesses; however, the data regarding the safety of these medications during pregnancy are inconclusive and contradictory. We examined the risk of adverse pregnancy outcomes associated with in utero exposure to SGAs by conducting a systematic review and meta-analysis. We searched the databases EMBASE and MEDLINE from January 1990 to December 2014. Eligible studies had to report pregnant women who took SGAs during pregnancy (first trimester exposure if analyzing congenital malformations), follow a healthy comparison group in a similar manner, and report data on pregnancy outcomes. There was no restriction on language, sample size, or publication date. The primary outcome analyzed was major congenital malformations, and secondary outcomes included miscarriages, stillbirths, preterm births, small or large for gestational age neonates, and differences in gestational ages and birth weights. A total of 12 studies met our inclusion criteria, totalling 1782 cases and 1,322,749 controls. The use of SGA during the first trimester of pregnancy was associated with a significant increased risk for major congenital malformations (odds ratio, 2.03; 95% confidence interval, 1.41-2.93); however, no specific pattern of malformations was found. An increased risk was also found for preterm births (odds ratio, 1.85; 95% CI, 1.20-2.86). The use of SGA during pregnancy was not found to be associated with an increased risk for secondary outcomes analyzed. The absence of a specific pattern of malformations makes it difficult to identify an explicit risk posed by SGAs, and therefore, further studies sufficiently controlling for confounding factors are needed to validate 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".