Variations in Neurodevelopmental Outcomes in Children with Prenatal SSRI Antidepressant Exposure
Bibliographic record
Abstract
Mood disorders and treatment with selective serotonin reuptake inhibitor (SSRI) antidepressants during pregnancy are common and both pose neurodevelopmental risks. This often makes the decision to treat prenatal depression with pharmacotherapy (i.e., antidepressants) challenging for clinicians and mothers. SSRIs block the reuptake of the neurotransmitter serotonin (5HT) and given its developmental role, it is not inconceivable that early changes in 5HT signaling could have an impact on early brain development. Identifying long-term neurodevelopmental effects of prenatal SSRI exposure is challenging in humans due to difficulties in distinguishing the effect of the drug from mother's mood during pregnancy and everyday environment in which the child lives, all of which contribute to shaping emotional, cognitive, and social development long after birth. In this review, we focus on the long-term neurobehavioral effects in childhood illustrating wide variations in outcomes revealing that some, but not all children appear to be affected by prenatal SSRI exposure. Emerging research reports findings that are beginning to distinguish the impact of genetic factors and the environment from prenatal medication exposure. Future research is needed to identify genetic, maternal, and environmental factors that put some children at developmental risk and others who may even benefit from maternal SSRI treatment. Birth Defects Research 109:909-923, 2017.© 2017 Wiley Periodicals, Inc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".