Adverse effects of antidepressant use in pregnancy: an evaluation of fetal growth and preterm birth
Bibliographic record
Abstract
OBJECTIVE: To compare the rates of low birth weight, preterm delivery and small for gestational age (SGA), in pregnancy outcomes among women who were exposed and nonexposed to antidepressants during pregnancy. METHODS: At The Motherisk Program, we analyzed pregnancy outcomes of 1,243 women in our database who took various antidepressants during their pregnancy. Nine hundred and twenty-eight of these women and 928 nonexposed women who delivered a live born infant were matched for age, (+/-2 years), smoking and alcohol use and specific pregnancy outcomes were compared between the two groups. RESULTS: There were 82 (8.8%) preterm deliveries in the antidepressant group and 50 (5.4%) in the comparison group. OR: 1.7 (95% CI: 1.18-2.45). There were 89 (9.6%) in the antidepressant group and 76 (8.2%) in the comparison group who delivered babies evaluated as SGA; OR: 1.19 (95% CI: 0.86-1.64). The mean birth weight in the antidepressant group was 3,449+/-591 g and 3,455+/-515 g in the comparison group (P=.8). CONCLUSION: The use of antidepressants in pregnancy appears to be associated with a small, but statistically significant increased rate in the incidence of preterm births, confirming results from several other studies. It is difficult to ascertain whether this small increased rate of preterm births is confounded by depression, antidepressants, or both. However, we did not find a statistically significant difference in the incidence of SGA or lower birth weight. This information adds to limited data available in the literature regarding these outcomes following the use of antidepressants in pregnancy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".