Bibliographic record
Abstract
BACKGROUND: Pregnancy registries are a new method for assessing the fetal risks from exposures in pregnancy. We present the findings of the North American AED (antiepileptic drug) Pregnancy Registry for phenobarbital sodium-exposed pregnancies. OBJECTIVE: To determine whether exposure during pregnancy to anticonvulsant drugs as monotherapy, and phenobarbital in particular, is associated with an increased risk of major malformations in comparison with unexposed controls. DESIGN: Evaluation of registry data. SETTING: The North American AED Pregnancy Registry. PATIENTS: Pregnant women throughout the United States and Canada who were taking an anticonvulsant drug and who called a toll-free telephone number to enroll. INTERVENTIONS: Each woman was interviewed by telephone at enrollment, at 7 months' gestation, and post partum. With the mother's written permission, her medical records and those of her infant were obtained. MAIN OUTCOME MEASURES: Major malformations identified by 5 days of age. Criteria for the release of findings were established by the independent Scientific Advisory Committee on the basis of malformations identified in infants of women who had enrolled prospectively before having had any prenatal screening ("pure" enrollees). RESULTS: Five (6.5%) of 77 pure pregnancies with exposure to phenobarbital monotherapy were associated with major malformations (95% confidence interval of proportion, 2.1%-14.5%). When compared with the background rate (1.62%), there was a significantly increased risk (relative risk, 4.2; 95% confidence interval, 1.5-9.4). CONCLUSIONS: A hospital-based pregnancy registry can establish the fetal risk of major malformations for a commonly used drug. Prenatal exposure to phenobarbital is associated with a significantly increased risk of fetal abnormalities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".