Association between reported venlafaxine use in early pregnancy and birth defects, national birth defects prevention study, 1997–2007
Bibliographic record
Abstract
BACKGROUND: Few epidemiologic studies have investigated the use of venlafaxine (Effexor XR capsules, Product Monograph, Wyeth, Montreal, Canada), an antidepressant used to treat major depression and anxiety disorders in adults, during pregnancy. Our objective was to determine whether use of venlafaxine during pregnancy is associated with specific birth defects. METHODS: We used data from the National Birth Defects Prevention Study (NBDPS), a population-based, case-control study in the United States. Our analysis included mothers with pregnancies affected by one of 30 selected birth defects (cases) and babies without birth defects (controls) with estimated dates of delivery between 1997 and 2007. Exposure was any reported use of venlafaxine from 1 month preconception through the third month of pregnancy. We calculated adjusted odds ratios (aORs) and 95% Fisher Exact confidence intervals (CIs) for 24 birth defect groups for which at least 400 case mothers were interviewed. Our adjusted analyses controlled for maternal age and race/ethnicity. RESULTS: Among the 27,045 NBDPS participants who met inclusion criteria, 0.17% (14/8002) of control mothers and 0.40% (77/19,043) of case mothers reported any use of venlafaxine from 1 month preconception through the third month of pregnancy. Statistically significant associations were found for anencephaly, atrial septal defect (ASD) secundum, or ASD not otherwise specified, coarctation of the aorta, cleft palate, and gastroschisis. CONCLUSIONS: Our data suggest associations between periconceptional use of venlafaxine and some birth defects. However, sample sizes were small, CIs were wide, and additional studies are needed to confirm these results.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".