Maternal Medication Use in Pregnancy: A Narrative Review on Assessing and Communicating the “Risk” of Birth Defects to the Patient
Bibliographic record
Abstract
The state of knowledge regarding the teratogenic effects of maternal use of medications during pregnancy is constantly evolving and is often uncertain. Timely access to high-quality information may reduce prolonged harmful exposures, decrease the number of preventable birth defects, empower patients with accurate information about the risks of exposure, and prevent unnecessary patient anxiety and pregnancy termination. In this narrative review, we describe the process by which the teratogenic risk of medications is assessed by experts in medicine, genetics, and epidemiology and how identifiable risks can be effectively communicated to patients. Risk assessment of birth defects in human pregnancy involves collecting and synthesizing available data through a proper and rule-driven evaluation of scientific literature. Expert consensus is a practical approach to determine whether a given exposure produces damage after careful consideration of gestational timing, dose and route of the exposure, maternal and fetal genetic susceptibility, as well as evidence for biological plausibility. The provision of teratogen risk counseling through appropriate interpretation of information and effective knowledge translation to the patient is critical for the prevention of birth defects and maximizing healthy pregnancies.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".