Neonatal and childhood neurodevelopmental, health and educational outcomes of children exposed to antidepressants and maternal depression during pregnancy: protocol for a retrospective population-based cohort study using linked administrative data
Bibliographic record
Abstract
INTRODUCTION: Antidepressants are commonly prescribed during pregnancy; however, there are inconsistent data on the safety of these medications during the prenatal period. To address this gap, this study will investigate short-term and long-term neurodevelopmental, physical and mental health, and educational outcomes of children who have been exposed to selective serotonin reuptake inhibitors (SSRIs) or selective serotonin norepinephrine reuptake inhibitors (SNRIs) and/or maternal depression during pregnancy. METHODS AND ANALYSIS: Administrative data will be linked to generate 4 population-based exposed groups from all children born in Manitoba between 1996 and 2014 whose mother had at least 2 prescriptions for either an SSRI or SNRI: (1) throughout the prenatal period (beginning of pregnancy until birth); (2) in the first trimester (≤14 weeks gestation); (3) in the second trimester (15-26 weeks gestation); (4) in the third trimester (≥27 weeks gestation) and 1 population-based unexposed group consisting of children whose mothers had a diagnosis of mood or anxiety disorder during pregnancy but did not use antidepressants. Propensity scores and inverse probability treatment weights will be used to adjust for confounding. Multivariate regression modelling will determine whether, compared with untreated mood/anxiety disorder, prenatal exposure to antidepressant medications is associated with: (1) adverse birth and neonatal outcomes, including: preterm birth, low birth weight, low Apgar scores, respiratory distress, congenital malformations and persistent pulmonary hypertension; (2) adverse early childhood outcomes, including: early childhood education challenges, diagnosis of neurodevelopmental disorders and diagnosis of mental disorders. We will determine if exposure effects differ between SSRIs and SRNIs, and determine if exposure effects differ between gestation timing of exposure to antidepressants. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Manitoba Health Research Ethics Board. Dissemination of results will include engagement of stakeholders and patients, writing of reports for policymakers and patients, and publication of scientific papers.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".