PW01-259 - Paroxetine Use During Pregnancy and Adverse Pregnancy Outcomes In The Absence Of Detection Bias
Bibliographic record
Abstract
Objective To quantify the prevalence of adverse pregnancy outcomes associated with gestational exposure to paroxetine before the FDA warnings on the risk of cardiac malformations in newborns associated with the use of paroxetine during gestation were issued in 2005. Methods Data on all pregnancies included in the Quebec Pregnancy Registry were analysed to estimate the prevalence of planned/spontaneous abortions and deliveries during 1998-2004, before the warnings were issued. All study outcomes including congenital malformations are physician-based, and made prospectively and routinely as part of health care management; exposure to paroxetine is based on automated prescription fillings. Trends in the frequency of gestational use of paroxetine during the study period were compared to the trends in the prevalence of cardiac malformations. Results Among the 109,344 eligible pregnancies, 1,612 (1.5%) were exposed to paroxetine. Amongst paroxetine users, 49% had a planned abortion, 7% a spontaneous abortion, and 44% a delivery. Amongst those who delivered, 3% had a baby with a cardiac malformation. The most prevalent cardiac malformations were atrial and ventricular septal defects (74%). During the study period, an increase in the prevalence of paroxetine use during pregnancy was correlated with an increase in the rate of cardiac malformations. Conclusion Paroxetine use during gestation is associated with an important risk of adverse pregnancy events above baseline rates. Before the warnings on paroxetine were issued, the increase in the prevalence of paroxetine use during pregnancy was already correlated with an increase in the rate of cardiac malformations, excluding the possibility of detection bias.
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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 | 0.039 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".