Major congenital malformations following prenatal exposure to serotonin reuptake inhibitors and benzodiazepines using population‐based health data
Bibliographic record
Abstract
BACKGROUND: To determine a population-based incidence of congenital anomalies following prenatal exposure to serotonin reuptake inhibitor (SRI) antidepressants used alone and in combination with a benzodiazepines (BZ). METHODS: Population health data, maternal health, and prenatal prescription records were linked to neonatal records, representing all live births (British Columbia, Canada, N=119,547) during a 39-month period (1998-2001). The incidence and risk differences (RD) for major congenital anomalies (CA) and congenital heart disease (CHD), including ventricular and atrial septal defects (VSD, ASD), from infants of mothers treated with an SRI alone, a benzodiazepine (BZ) alone, or SRI+BZ in combinationcompared to outcomesno exposure. RESULTS: Risk for a CA or CHD did increase following combined SRI+BZ exposure compared with no exposure. However, using a weighted regression model, controlling for maternal illness characteristics, combination therapy risk remained significantly associated only with CHD. The risk for an ASD was higher following SRI monotherapy compared with no exposure, after adjustment for maternal covariates. Dose/day was not associated with increased risk. CONCLUSIONS: Infants exposed to prenatal SRIs in combination with BZs had a higher a incidence of CHD compared to no exposure, even after controlling for maternal illness characteristics. SRI monotherapy was not associated with an increased risk for major CA, but was associated with an increased incidence of ASD. Risk was not associated with first trimester medication dose/day.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".