Antidepressant Use during Pregnancy and the Rates of Spontaneous Abortions: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Due to the high prevalence of depression in women of childbearing age and coupled with the fact that approximately 50% of the pregnancies are unplanned, there is a high chance that these women have been exposed to antidepressants in early pregnancy. OBJECTIVE: To determine baseline rates of spontaneous abortions (SAs) and whether antidepressants increase those rates. METHODS: Rates of SAs in women taking antidepressants compared with non-depressed women were combined into a relative risk using a random effects model. MEDLINE, EMBASE, Healthstar, Toxline, Psychlit, Cochrane database, and Reprotox were searched for studies published in any language from 1966 to 2003. Key words used to identify articles included pregnancy outcome, abortion, miscarriage, spontaneous, antidepressant, depression, and the generic names of each antidepressant and class. Bibliographies, review articles, and reference lists from studies were also used to identify potential articles expected to provide evidence of safety of antidepressants in pregnancy. RESULTS: Of 15 potential articles, 6 cohort studies of 3567 women (1534 exposed, 2033 nonexposed) provided extractable data. All matched on important confounders. Tests found no heterogeneity (chi2 3.13; p = 0.98), and all quality scores were adequate (>50%). The baseline SA rate (95% CI) was 8.7% (7.5% to 9.9%; n = 2033). For antidepressants, the rate was 12.4% (10.8% to 14.1%; n = 1534), significantly increased by 3.9% (1.9% to 6.0%); RR was 1.45 (1.19 to 1.77; n = 3567). No differences were found among antidepressant classes. CONCLUSIONS: Maternal exposure to antidepressants may be associated with increased risk for SA; however, depression itself cannot be ruled out.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.060 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".