Association between Antidepressant Use during Pregnancy and Infants Born Small for Gestational Age
Bibliographic record
Abstract
Objective To measure the association between the class of antidepressant (AD) used according to trimester of exposure during pregnancy and infants born small for gestational age (SGA). Methods A case–control study was performed using data from the Quebec Pregnancy Registry, which includes 152 107 pregnant women between January 1, 1998, and December 31, 2002. For this study, eligible women were aged 15 to 45 years on the first day of gestation, had drug plan coverage from the Régie de l'Assurance Maladie du Québec for 12 months or more prior to and during pregnancy, had at least 1 psychiatric disorder diagnosis before pregnancy, used ADs for at least 30 days in the year prior to pregnancy, and delivered a live singleton. AD exposure during pregnancy was defined according to trimester of use and class (selective serotonin reuptake inhibitors [SSRIs], tricyclic antidepressants, or other ADs). SGA cases were defined as newborns with a birth weight of less than the 10th percentile according to Canadian charts. Relative risks, adjusted for potential confounders, were estimated using modified Poisson regression. Results Among the 938 eligible pregnancies, 128 (13.6%) infants were born SGA. Other ADs, mainly venlafaxine, used by women during the second trimester were associated with an increased risk of infants born SGA, compared with nonusers of ADs (adjusted relative risk = 2.41; 95% CI 1.07 to 5.43). Regardless of the trimester of use, no association was found between SSRIs or tricyclics and the risk of SGA. Conclusions This study suggests that use of venlafaxine during the second trimester of pregnancy may increase the risk of infants born SGA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".