Pregnancy Outcomes Following Use of Escitalopram: A Prospective Comparative Cohort Study
Bibliographic record
Abstract
Escitalopram is a serotonin reuptake inhibitor prescribed for depression and anxiety. There is a paucity of information regarding safety in pregnancy. The objective of this study was to determine whether escitalopram is associated with an increased risk for major malformations or other adverse outcomes following use in pregnancy. The authors analyzed pregnancy outcomes in women exposed to escitalopram (n = 212) versus other antidepressants (n = 212) versus nonteratogenic exposures (n = 212) and compared the outcomes. Among the escitalopram exposures were 172 (81%) live births, 32 (15%) spontaneous abortions, 6 (2.8%) therapeutic abortions, 3 stillbirths (1.7%), and 3 major malformations (1.7%). The only significant differences among groups was the rate of low birth weight (<2500 g) and overall mean birth weight (P = .225). However, spontaneous abortion rates were higher in both antidepressant groups (15% and 16%) compared with controls (8.5%; P = .066). There were lower rates of live births (P = .006), lower overall birth weight (P < .001), and increased rates of low birth weight (<2500 g; P = .009) with escitalopram. Spontaneous abortion rates were nearly double in both antidepressant groups (15% and 16%) compared with controls (8.5%) but not significant (P = .066). Escitalopram does not appear to be associated with an increased risk for major malformations but appears to increase the risk for low birth weight, which was correlated with the increase in infants weighing <2500 g. In addition, the higher rates of spontaneous abortions in both antidepressant groups confirmed previous 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".