A longitudinal analysis of the predictors and consequences of prenatal antidepressant use among women requiring these medications before pregnancy
Bibliographic record
Abstract
Objective: To explore the characteristics associated with antidepressant discontinuation in pregnancy compared to non-pregnant women taking antidepressants.Methods: Pregnant women delivering between January 1 st , 1998 and December 31 st , 2002 were identified using Quebec's health administration databases.Women with at least one prescription for an antidepressant in the six months before pregnancy were matched to up to three non-pregnant women on age and date of first prescription filled in the pre-pregnancy period.Women were considered to have discontinued medication if they had filled at least one prescription in the six month pre-pregnancy period, and then had no prescriptions filled during pregnancy.Multivariable log binomial regression was used to assess the association between demographic, health and medication characteristics, and antidepressant discontinuation in pregnancy.We also assessed the risk of hospitalization in stoppers vs. continuers using propensity score analysis.Results: Pregnant women were 4.96 (95% CI: 4.30 to 5.72) times more likely than non-pregnant women to discontinue antidepressant use, and 53% of pregnant women discontinued all antidepressant use in pregnancy.Pregnant women were more likely to discontinue antidepressant use if they were younger, not receiving welfare, and had a shorter duration of pre-pregnancy antidepressant use.Women receiving TCAs, MAOIs or atypical antidepressants before pregnancy were more likely to discontinue than those on SSRI monotherapy.Discontinuers were less likely to be hospitalized for mental health problems after 16 weeks of gestation compared to women with continuous antidepressant use in early pregnancy (0.22; 95%CI: (0.08, 0.68). Conclusion:Our results suggest that pregnant women with less severe disease are more likely to discontinue treatment, but because pregnancy itself is a major predictor of discontinuation, physicians need to pay particular attention to pregnant women requiring pre-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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".