Gestational Exposure to Antidepressants and the Risk of Spontaneous Abortion: A Review
Bibliographic record
Abstract
BACKGROUND: Although the relationship between antidepressant use during pregnancy and its adverse effects has been widely investigated, very few studies have evaluated the impact of antidepressant use during pregnancy on the risk of spontaneous abortion. We present an overview of the evidence relating to the association between antidepressant use during gestation and the risk of spontaneous abortion. METHODS: We systematically searched PubMed and the reference lists of all relevant articles, including reviews, published in English or French from 1975 through 2009 for studies that examined the association between adverse pregnancy outcomes and gestational exposure to antidepressants with data on spontaneous abortions. Only etiologic studies were considered. RESULTS: Fifteen studies met inclusion criteria. The majority of these were prospective cohort studies on tricyclics antidepressants (TCAs) or selective serotonin reuptake inhibitors (SSRIs) use during pregnancy. Overall, in unadjusted analyses, fluoxetine (OR = 2.0; 95% CI = 1.4 - 3.0) and bupropion (OR = 4.1; 95% CI = 1.5 - 11.1) were significantly associated with the risk of spontaneous abortion. However, in adjusted analyses, only paroxetine (OR = 1.7; 95% CI = 1.3 - 2.3) and venlafaxine (OR = 2.1; 95% CI = 1.3 - 3.3) were significantly associated with the risk of spontaneous abortion. CONCLUSIONS: This review suggests that gestational exposure to antidepressants, especially paroxetine and venlafaxine, can lead to spontaneous abortion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".