The Association Between Antenatal Exposure to Selective Serotonin Reuptake Inhibitors and Autism
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis examines the relationship between antenatal selective serotonin reuptake inhibitor (SSRI) exposure and child autism, with specific attention to maternal mental illness (MMI) as a potential confounding factor. DATA SOURCES: We searched MEDLINE, Embase, PsycINFO, and CINAHL from database inception to January 28, 2016. STUDY SELECTION: Keywords included terms for SSRIs, pregnancy, and autism. We included published, peer-reviewed articles written in English. DATA EXTRACTION: Two reviewers used standardized instruments for data extraction and quality assessment. We generated pooled estimates for studies of the same design for SSRI exposure at any time during pregnancy and exposure during the first trimester. Subanalyses were conducted among studies with analyses (1) adjusted for MMI and (2) restricted to MMI. RESULTS: We included in the meta-analysis 4 case-control studies and 2 cohort studies. In the case-control studies, the adjusted pooled odds ratio (aPOR) values were 1.4 (95% CI, 1.0-2.0) (any) and 1.7 (95% CI, 1.1-2.6) (first trimester). In MMI-adjusted analyses, only first trimester exposure remained statistically significant (aPOR = 1.8; 95% CI, 1.1-3.1). In MMI-restricted analyses, neither exposure period was statistically significant. In the cohort studies, MMI-adjusted relative risk values were 1.5 (95% CI, 0.9-2.7) (any) and 1.4 (95% CI, 1.0-1.9) (first trimester). In MMI-restricted analyses, SSRI exposure at any time during pregnancy was nonsignificant. CONCLUSIONS: It remains unclear whether the association between first trimester SSRI exposure and child autism that was present in the case-control studies even after adjustment for MMI is a true association or a product of residual confounding. Future studies require robust measurement of MMI prior to and during pregnancy.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".