Factors associated with re-initiation of antidepressant treatment following discontinuation during pregnancy: a register-based cohort study
Bibliographic record
Abstract
Antidepressant treatment when facing a pregnancy is an important issue for many women and their physicians. We hypothesized that women with a greater burden of pre-pregnancy psychiatric illness would be more likely to re-initiate antidepressants following discontinuation of treatment during pregnancy. A register-based cohort study was carried out including 38,595 women who gave birth between the 1st of January 2007 and the 31st of December 2014, who had filled a prescription for an antidepressant medication in the year prior to conception. Logistic regressions were used to explore associations between maternal characteristics and antidepressant treatment discontinuation or re-initiation during pregnancy. Most women discontinued antidepressant treatment during pregnancy (n = 29,095, 75.4%), of whom nearly 12% (n = 3434, 11.8%) re-initiated treatment during pregnancy. In adjusted analyses, parous women (aOR 1.22, 95% CI 1.12-1.33), with high educational level (aOR 1.21, 95% CI 1.08-1.36); born within the EU (excluding Nordic countries, aOR 1.41, 95% CI 1.03-1.92) or a Nordic country (aOR 1.42, 95% CI 1.22-1.65); who more often reported prior hospitalizations due to psychiatric disorders (aOR 1.50, 95% CI 1.10-2.03, for three or more episodes); and had longer duration of pre-pregnancy antidepressant use (aOR 6.10, 95% CI 5.48-6.77, for >2 years antidepressant use), were more likely to re-initiate antidepressants than were women who remained off treatment. Women with a greater burden of pre-pregnancy psychiatric illness were more likely to re-initiate antidepressants. Thus, pre-pregnancy psychiatric history may be particularly important for weighing the risks and benefits of discontinuing antidepressants 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".