Paroxetine Use During Pregnancy and Perinatal Outcomes Including Types of Cardiac Malformations in Quebec and France: A Short Communication
Bibliographic record
Abstract
BACKGROUND: Antidepressants, more specifically SSRIs, represent one example of a widely prescribed class of medications in pregnant women for which less than adequate pregnancy data have been available since the first drug in this class was marketed 20 years ago. Moreover, findings from studies performed after 2005, when health governmental authorities issued warnings regarding first trimester exposure to paroxetine and the risk of cardiac malformations, may be the result of detection bias if physicians were investigating more their pregnant patients that used paroxetine than the others. OBJECTIVES: To estimate the prevalence of 1) paroxetine use during pregnancy, and 2) diagnosed cardiac malformations in the Quebec and France populations. METHODS: Two distinct pregnancy databases were used for this ecologic study: the Quebec Pregnancy Registry and the French EFEMERIS database. RESULTS: In Quebec, among the 109,344 eligible pregnancies, 1,612 (1.5%) were exposed to paroxetine. Prevalence of paroxetine use during pregnancy increased from 0.7% to 1.2% between 1998 and 2003, simultaneously to the increase of the prevalence of cardiac malformation diagnoses. In France, among 40,317 eligible pregnancies, 173 (0.4%) were exposed to paroxetine; between 2004 and 2008 the number of paroxetine fillings and cardiac malformation diagnoses remained constant. CONCLUSIONS: Despite differences in the Quebec and French healthcare systems, increase in paroxetine prevalence use during pregnancy was already consistent with an increase in the prevalence of cardiac malformations, even before the warning on the risk of cardiac malformations in newborns in 2005-2006, limiting the possibility of detection bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".