Effect of prenatal selective serotonin reuptake inhibitor (SSRI) exposure on birthweight and gestational age: a sibling-controlled cohort study
Bibliographic record
Abstract
Background: Up to 10% of women are exposed to selective serotonin reuptake inhibitors (SSRIs) during pregnancy. Information on their effect on birthweight and gestational age remains conflicting. The aim of this sibling-controlled prospective cohort study is to address shared geneticand family-level confounding to investigate the effects of prenatal SSRI exposure and maternal depression on birthweight and gestational age. Methods: We used the Norwegian Mother and Child Cohort Study (MoBa) and the Medical Birth Registry of Norway (MBRN). Our study population consisted of 27 756 siblings; 194 were prenatally exposed to SSRIs and 27 500 were unexposed to any antidepressant medication. Random and fixed effects analysis with propensity score adjustment was used to evaluate the effectson birthweight and gestational age. Results: SSRI exposure during two or more trimesters was associated with a decrease in birthweight of 205 g [95% confidence interval (CI) -372 to - 38] and a decrease in gestational length of 4.9 days (95% CI - 9.1 to - 1.4). Neither maternal SSRI use in one trimester, lifetime history of major depression nor depressive symptoms during pregnancy were associated with these pregnancy outcomes (for non-pharmacologically treated depression in two periods in pregnancy, +5 g (95% CI - 56 to + 67) and +4.9 days (95% CI - 4.7 to + 14.7), respectively). Conclusions: Prenatal exposure to SSRIs during two or more trimesters may decrease birthweight and gestational length. Our results indicate that neither maternal depression nor shared genetics and family environment fully explain this association.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".