Trajectories of antidepressant drugs during pregnancy: A cohort study from a community‐based sample
Bibliographic record
Abstract
AIMS: The aim of this study was to monitor the trajectories of antidepressant use during pregnancy and the postpartum period among women chronically treated with antidepressants before their pregnancy, and to assess characteristics associated with each trajectory. METHODS: This cohort study included all pregnant women whose data were included in the General Sample of Beneficiaries (EGB) database affiliated with the French Health Insurance System, from 2009 to 2014. Women were followed up until 6 months after childbirth. Chronic treatment was defined as exposure over the 6-month period preceding pregnancy. A group-based trajectory model (GBMT) was estimated to identify distinctive longitudinal profiles of antidepressant use. RESULTS: Among 760 women chronically treated with antidepressants before their pregnancy, 55.8% stopped their treatment permanently in the first trimester, 20.4% discontinued it for a minimum of 3 months and resumed it postpartum, and 23.8% maintained it throughout pregnancy and postpartum. No sociodemographic or medical characteristics were associated with any trajectory group. Women who maintained treatment presented more frequent obstetric complications and postpartum psychiatric disorders. Among women who interrupted treatment, prescription of benzodiazepines and anxiolytics decreased initially but rose postpartum to a higher level than before pregnancy. CONCLUSIONS: Pregnant women treated with antidepressant require a re-evaluation of psychiatric treatment. It is necessary to pay attention to obstetric complications for severely depressed women. Additionally, as relapse was associated with increased benzodiazepine use, it is important to carefully monitor all women who stop antidepressant treatment during pregnancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".