In Utero Antidepressants and Neurodevelopmental Outcomes in Kindergarteners
Bibliographic record
Abstract
OBJECTIVES: To determine if in utero selective serotonin reuptake inhibitor (SSRI) or selective serotonin norepinephrine inhibitor (SNRI) exposure is associated with developmental vulnerability in kindergarten among children whose mothers were diagnosed with prenatal mood or anxiety disorder. METHODS: Linkable administrative data were used to create a population-based cohort of 266 479 mother-child dyads of children born in Manitoba, Canada, between 1996 and 2014, with follow-up through 2015. The sample was restricted to mothers who had a mood or anxiety disorder diagnosis between 90 days before conception (N = 13 818). Exposed women had ≥2 SSRI or SNRI dispensations during pregnancy (n = 2055); unexposed mothers did not have a dispensation of an SSRI or SNRI during pregnancy (n = 10 017). The Early Development Instrument (EDI) was used to assess developmental health in kindergarten children. The EDI is a 104-component kindergarten teacher-administered questionnaire, encompassing 5 developmental domains. RESULTS: Of the 3048 children included in the study who met inclusion criteria and had an EDI, 21.43% of children in the exposed group were assessed as vulnerable on 2 or more domains versus 16.16% of children in the unexposed group (adjusted odds ratio = 1.43; 95% confidence interval 1.08–1.90). Children in the exposed group also had a significant risk of being vulnerable in language and/or cognition (adjusted odds ratio = 1.40; 95% confidence interval 1.03–1.90). CONCLUSIONS: Exposure to SSRIs or SNRIs during pregnancy was associated with an increased risk of developmental vulnerability and an increased risk of deficits in language and/or cognition. Replication of results is necessary before clinical implications can be reached.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".