Maternal Medication Use in Pregnancy: A Narrative Review on Assessing and Communicating the “Risk” of Birth Defects to the Patient
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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