Prescriptions filled during pregnancy for drugs with the potential of fetal harm
Bibliographic record
Abstract
OBJECTIVE: To assess the extent of prescriptions filled by pregnant women for drugs with recognised potential of fetal harm, and to document the outcomes of these pregnancies. DESIGN: Cross-sectional study. POPULATION: Quebec Pregnancy Registry. METHODS: We identified women who were pregnant during a five-year period and who were insured for prescription medications under the provincial drug plan. We obtained information on prescriptions filled during pregnancy for drugs with known potential of fetal harm. MAIN OUTCOME MEASURES: Prescriptions filled for study drugs during the first, second and third trimesters of pregnancy; termination of pregnancy (TOP) or delivery, and whether the baby was diagnosed with a major congenital malformation (MCM). RESULTS: Of 109 344 women, 56% filled at least one prescription for a medication during pregnancy; 6.3% filled at least one prescription for a drug known to pose a risk to the fetus. Overall, 47% (95% CI, 45.8-48.2) of pregnancies exposed to drugs under study ended in TOP versus 36.2% (95% CI, 35.9-36.5) of those not exposed; 8.2% (95% CI, 8.0-10.0) of live births were diagnosed with an MCM during the first year of life versus 7.1% (95% CI, 6.9-7.3) of those not exposed. CONCLUSIONS: This study documents an important level of prescriptions filled during pregnancy for drugs harmful to the developing fetus. The proportions of both TOPs and babies born with MCMs were elevated compared with the expected values. Clinicians caring for women during pregnancy should conduct a medication inventory prior to a planned pregnancy, or as soon as an unplanned pregnancy is recognised.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".