A systematic review of maternal antidepressant use in pregnancy and short- and long-term offspring’s outcomes
Bibliographic record
Abstract
The relative safety of antidepressants during pregnancy has received substantial attention, but most syntheses fail to account for mental illness effects. We aimed to evaluate the literature comparing low birth weight (LBW) and neurodevelopmental and neurobehavioural outcomes for children whose mothers took antidepressants in pregnancy compared to those whose mothers had common mental disorders, or symptoms, but who did not take antidepressants during pregnancy. A systematic review was conducted searching PubMed, MEDLINE, PsycINFO and Embase in January 2015. A modified version of the Newcastle Ottawa Scale was used to assess study quality. Eleven cohort studies were included: four reporting a LBW outcome (all with higher risk of bias) and seven reporting a neurodevelopmental outcome (five with higher risk of bias). We found only limited evidence of gestational age-adjusted LBW in exposed children in two studies which had a higher risk of bias and did not control for depressive symptom severity. Only five (7.5%) neurodevelopmental outcomes and one (12.5%) neurobehavioural outcome showed evidence of a statistically significant effect, three out of four were from studies with a higher risk of bias. There is little robust evidence indicating a detrimental effect of antidepressant use during pregnancy on LBW and neurodevelopmental and neurobehavioural outcomes. More rigorous study designs are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".