Medications in the first trimester of pregnancy: most common exposures and critical gaps in understanding fetal risk
Bibliographic record
Abstract
PURPOSE: To determine which medications are most commonly used by women in the first trimester of pregnancy and identify the critical gaps in information about fetal risk for those medications. METHODS: Self-reported first-trimester medication use was assessed among women delivering liveborn infants without birth defects and serving as control mothers in two large case-control studies of major birth defects. The Teratology Information System (TERIS) expert Advisory Board ratings of quality and quantity of data available to assess fetal risk were reviewed to identify information gaps. RESULTS: Responses from 5381 mothers identified 54 different medication components used in the first trimester by at least 0.5% of pregnant women, including 31 prescription and 23 over-the-counter medications. The most commonly used prescription medication components reported were progestins from oral contraceptives, amoxicillin, progesterone, albuterol, promethazine, and estrogenic compounds. The most commonly used over-the-counter medication components reported were acetaminophen, ibuprofen, docusate, pseudoephedrine, aspirin, and naproxen. Among the 54 most commonly used medications, only two had "Good to Excellent" data available to assess teratogenic risk in humans, based on the TERIS review. CONCLUSIONS: For most medications commonly used in pregnancy, there are insufficient data available to characterize the fetal risk fully, limiting the opportunity for informed clinical decisions about the best management of acute and chronic disorders during pregnancy. Future research efforts should be directed at these critical knowledge gaps.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".