Antidepressant Use During Pregnancy: A Critical Systematic Review of the Literature
Bibliographic record
Abstract
Over the past 15 years, the number of studies investigating the potential teratogenic effects of antidepressants has drastically increased. Prescribing antidepressants during pregnancy is becoming a challenge for health care providers because of conflicting data on their teratogenic potential. A critical systematic review of studies describing the relationship between antidepressant use during pregnancy and its impact on congenital malformations, prematurity, low birth weight (LBW), and child development was undertaken to summarize the current evidence-based findings. Most antidepressants do not pose a major teratogenic risk, although the data supporting this conclusion vary from one type to another. While SSRIs and tricyclics have been examined in a considerable number of studies, only scarce data is available on new antidepressants. The use of paroxetine during organogenesis has been linked to an increase in the risk of cardiovascular malformations. The impact of prenatal exposure to antidepressants on prematurity and LBW remains controversial, and most studies evaluating these outcomes are limited by their small sample size and lack of adequate reference group. Finally, information on the long-term effects of gestational antidepressant use on child development is only starting to emerge, and existing information is too limited to determine the risk.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".