Newer antidepressants in pregnancy and rates of major malformations: a meta-analysis of prospective comparative studies
Bibliographic record
Abstract
BACKGROUND: A substantial number of women of childbearing age suffer from depression. Despite this, relatively little is known about the safety of antidepressant use during pregnancy. PURPOSE: We conducted a meta-analysis of prospective comparative cohort studies to quantify the relationship between maternal exposure to the newer antidepressants and major malformations. METHODS: We searched Medline, Embase and Reprotox from 1996 to the present for studies comparing outcomes in first trimester exposures to citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, reboxetine, venlafaxine, nefazodone, trazodone, mirtazapine and bupropion to those of non-exposed mothers. Data were combined using a random effects model; heterogeneity was tested with chi2, and publication bias with a funnel plot and the Begg-Mazumdar statistic. RESULTS: Twenty-two studies were identified, 15 were rejected (4 reviews, 4 without comparison groups, 2 third trimester exposures, 2 retrospective database studies, 2 case reports and 1 duplicate); 7 studies (n = 1774) met inclusion criteria. Effects were not heterogeneous (chi2 = 2.04, p = 0.92); funnel plot and test (tau = -0.24, p = 0.45) indicated no publication bias. The summary relative risk was 1.01 (95%CI: 0.57-1.80). CONCLUSIONS: As a group, the newer antidepressants are not associated with an increased risk of major malformations above the baseline of 1-3% in the population.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".