Risk of Major Malformations Following First-Trimester Exposure to Olanzapine
Bibliographic record
Abstract
PURPOSE/BACKGROUND: Since its US Food and Drug Administration approval in 1996, olanzapine has been one of the most commonly prescribed atypical antipsychotics, making a better understanding of its reproductive safety profile critical. The goal of the current analysis was to determine the risk of major malformations among infants exposed to olanzapine during pregnancy compared with a group of nonexposed infants. METHODS/PROCEDURES: The National Pregnancy Registry for Psychiatric Medications is a prospective pharmacovigilance program in which pregnant women are enrolled and interviewed during pregnancy and the postpartum period. Labor and delivery and pediatric medical records were screened for evidence of major malformations followed by adjudication by a dysmorphologist blinded to medication exposure. Infants with first-trimester exposure to olanzapine were compared with controls without second-generation antipsychotic exposure. FINDINGS/RESULTS: As of April 18, 2022, 2619 women have enrolled in the study. At the time of data extraction, 49 olanzapine-exposed infants and 1156 infants in the comparison group were eligible for these analyses. There were no major malformations associated with olanzapine exposure in the first trimester. The absolute risk for major malformations in the exposure group was 0.00% (95% confidence interval, 0.00-7.25) for olanzapine compared with 1.64% (95% confidence interval, 0.99-2.55) in the control group. IMPLICATIONS/CONCLUSIONS: In this prospective cohort, no major malformations were associated with olanzapine exposure during the first trimester. Although these data are preliminary and cannot rule out more modest effects, they are nonetheless important, adding to the growing reproductive safety data for olanzapine.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".