Counseling pregnant women treated with paroxetine. Concern about cardiac malformations.
Bibliographic record
Abstract
QUESTION: I have always reassured my patients that taking selective serotonin reuptake inhibitors (SSRIs) during pregnancy would not increase their risk of having children with major malformations. A recent warning from Health Canada, based on results of a study from GlaxoSmithKline, stated that infants exposed to paroxetine might be at higher risk of congenital malformations, specifically cardiovascular defects. Some of my pregnant patients who are taking paroxetine heard the warning and asked me whether they should stop taking it. What should I tell them? ANSWER: The new warning is based on unpublished, non-peer-reviewed studies. It ignored 2 published studies that failed to show any association between exposure to paroxetine and cardiovascular malformations, and no association with cardiovascular malformations has been shown by SSRIs as a class. Even if there is risk, it is minimal, and the warning does not disclose details of the cardiovascular malformations. Many cases of ventricular septal defect, the most common cardiac malformation, resolve spontaneously. Concerned pregnant women should know that, if taken after the first trimester, drugs cannot cause cardiac malformations. Failure to treat depression during pregnancy can have severe consequences for both mothers and babies and is the strongest predictor of postpartum depression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".