Links between serotonin reuptake inhibition during pregnancy and neurodevelopmental delay/spectrum disorders: A systematic review of epidemiological and physiological evidence
Bibliographic record
Abstract
OBJECTIVE: To investigate possible linkages between neurodevelopmental delay and neurodevelopmental spectrum disorders and exposure to medication with effects on serotonin reuptake inhibition during pregnancy. METHODS: We systematically reviewed the epidemiological literature for studies bearing on this relationship in children born with neurodevelopmental spectrum disorder and related conditions, as well as animal studies giving serotonin reuptake inhibitors to pregnant animals and in addition reviewed the literature for proposals as to possible mechanisms that might link effects on serotonin reuptake with cognitive changes post-partum.The epidemiological studies were analysed to produce Forest plots to illustrate possible relations. RESULTS: The odds ratio of Autistic Spectrum or related Disorders in children born to women taking serotonin reuptake inhibiting antidepressants during pregnancy in case control studies was 1.95 (95% C.I. 1.63, 2.34) and in prospective cohort studies was 1.96 (95% C.I. 1.33, 2.90). CONCLUSIONS: There appears to be a link between serotonin reuptake inhibition in pregnancy and developmental delay and spectrum disorders in infancy leading to cognitive difficulties in childhood. More work needs to be done to establish more precisely the nature of the difficulties and possible mechanisms through which this link might be mediated.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".