Implementation of Advanced Methods for Reproductive Pharmacovigilance in Autism: A Meta-Analysis of the Effects of Prenatal Antidepressant Exposure
Bibliographic record
Abstract
OBJECTIVE: Observational studies of prenatal antidepressant safety are hindered by methodological concerns, including susceptibility to surveillance bias. Some studies address potential bias by using alternative strategies to operationalize study comparison groups. In a meta-analysis of the association between prenatal antidepressant exposure and autism risk, the authors examined the utility of comparison group operationalization in reducing surveillance bias. METHODS: A systematic search of multiple databases through August 2017 was conducted, selecting controlled observational studies of the association of prenatal antidepressant exposure with autism. Study quality was assessed using the Newcastle-Ottawa Scale. Random-effects meta-analysis produced summary effect measures with 95% confidence intervals stratified by comparator group composition, antidepressant class, and trimester of exposure. RESULTS: Fourteen studies were included, with 13 reporting results using a population-based comparison group, five using a psychiatric control group, and four using a discordant-sibling control group. Eight of the 14 studies were rated poor because of inadequate control for prenatal depression and maternal ethnicity. Autism risk estimates after prenatal exposure to any antidepressant were decidedly different for population-based designs (hazard ratio=1.42, 95% CI=1.18, 1.70; odds ratio=1.58, 95% CI=1.25, 1.99) compared with psychiatric control (hazard ratio=1.14, 95% CI=0.84, 1.53; odds ratio=1.24, 95% CI=0.93, 1.66) and discordant-sibling (hazard ratio=0.97, 95% CI=0.68, 1.37; odds ratio=0.85, 95% CI=0.54, 1.35) designs. Findings for prenatal exposure to selective serotonin reuptake inhibitors were similar. Meta-regression of population-based studies demonstrated that despite statistical adjustment, ethnicity differences remained a significant source of study heterogeneity. CONCLUSIONS: In this meta-analysis, neither psychiatric control nor discordant-sibling designs supported an association between prenatal antidepressant exposure and autism. Discordant-sibling designs effectively addressed surveillance bias in pharmacovigilance reports derived from national registries and other large databases.
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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 | 0.090 | 0.190 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.067 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".