The Risk of Using Antidepressants During Pregnancy: Serious Concern or Much Ado about Little? [P02-24]
Bibliographic record
Abstract
Perinatal psychiatric disorders are a leading cause of maternal morbidity, they transmit across generations and may pose the first adverse life event for a child. The myth that pregnancy is “protective” in terms of mental health has long been refuted and it is now well established that the incidence of depression during pregnancy is at least as high when compared to other phases in a woman's lifetime. Moreover, depression during pregnancy has been associated with a number of adverse outcomes both for the mother and the baby. Untreated depression during pregnancy is also one of the strongest predictors of a subsequent postpartum depression. Data on the “relative safety” of antidepressants during pregnancy are accumulating but at the same time sporadic, at times inconsistent reports on potential risks associated with their use are cause for concern. Recognizing the limitations of our knowledge regarding the “relative safety” of antidepressants during pregnancy it is paramount to weigh the risk of not treating vs. the benefit of treating in each case. We report here on more than 400 pregnant women at risk for depression seen at our clinic. One third of these women received antidepressants during different phases of their pregnancies and the outcome/well-being of their babies assessed. There were no significant increases in long-term untoward outcomes in these babies. We also demonstrated in a smaller subpopulation that the use of antidepressants has a “positive” effect on both neuroendocrine and neurophysiological parameters, using the cortisol awakening response and heart rate variability as biological markers.
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".