S07-04 - Safety/risk of Psychiatric Drugs in Pregnancy and Lactation. Adrienne Einarson, the Motherisk Program
Bibliographic record
Abstract
Psychiatric disorders are relatively common among women of childbearing age, who may become pregnant while they are taking psychotropic drugs. There remains a high level of anxiety regarding their safety among women and healthcare providers alike, most likely because of the conflicting studies that have been published in the literature and warnings from government organizations. In addition, most recently in the case of paroxetine, a successful lawsuit against the manufacturer, holding them responsible for a baby born with a heart defect, following use of the drug by the mother in the first trimester of pregnancy. Consequently, treating a psychiatric disorder during pregnancy with pharmacotherapy, is a complex decision making process, which has to be made between the pregnant woman and her health care provider following careful evaluation of the evidence-based information. The objective of this presentation is two-fold, 1) to discuss models used for studying the use of drugs in pregnancy and 2) to provide current information on the safety/risk of psychotropic drugs used in pregnancy. The body of evidence in the literature to date suggests that psychotropic drugs as a group are relatively safe to take during pregnancy and women and their health care providers should not be unduly concerned if a women requires treatment. Optimal control of the psychiatric disorder should be maintained during pregnancy, the post partum period and thereafter. All pregnancies where a mother has a serious psychiatric disorder should be considered high risk and the mother and fetus, carefully monitored.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.019 |
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".