Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Norepinephrine Reuptake Inhibitors (SNRIs) During Pregnancy and the Risk for Autism spectrum disorder (ASD) and Attention deficit hyperactivity disorder (ADHD) in the Offspring: A True Effect or a Bias? A Systematic Review & Meta-Analysis
Bibliographic record
Abstract
Background and Objective: An inconsistent association between exposure to SSRIs and SNRIs and the risk for ASD and ADHD in the Offspring was observed in observational studies. Some suggest that the reported association might be due to unmeasured confounding. We aimed to study this association and to look for sources of bias by performing a systematic review and meta-analysis. Methods: Medline, Embase, and the Cochrane Library were searched up to June 2019 for studies reporting on ASD and ADHD in the Offspring following exposure during pregnancy. We followed the PRISMA 2009 guidelines for data selection and extraction. Outcomes were pooled using random- effects models and odds ratios (OR), and 95% confidence intervals (CI) were calculated for each outcome using the adjusted point estimate of each study. Results: Eighteen studies were included in the meta-analysis. We found an association between SSRIs/ SNRIs prenatal use and the risk for ASD and ADHD (OR=1.42, 95% CI: 1.23–1.65, I2=58%; OR=1.26, 95% CI: 1.07-1.49, I2=48%, respectively). Similar findings were obtained in women who were exposed to SSRIs/SNRIs before pregnancy, representing statistically significant association with ASD (OR=1.39, 95% CI: 1.24-1.56, I2=33%) and ADHD (OR=1.63, 95% CI: 1.50-1.78, I2=0%) in the Offspring, although they were not exposed to those medications in utero. Conclusions: Although we found an association between exposure to SSRIs/SNRIs during pregnancy and the risk for ASD and ADHD, an association with those disorders was also present for exposure pre-pregnancy, suggesting that the association might be due to unmeasured confounding. We are aiming to further assess the role of potential unmeasured confounding in the estimation of the association and perform a network meta-analysis.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".