Medications in the first trimester of pregnancy: most common exposures and critical gaps in understanding fetal risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it