Prescribed opioid analgesics in early pregnancy and the risk of congenital anomalies: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Recent data suggest an increased risk of congenital anomalies with prenatal exposure to opioid analgesics. We sought to further quantify the risk of anomalies after opioid analgesic exposure during the first trimester in a population-based cohort study. METHODS: Using administrative health data from Ontario, we followed 599 579 gestational parent-infant pairs from singleton pregnancies without opioid use disorder. We identified opioid analgesics dispensed in the first trimester and congenital anomalies diagnosed during the first year of life. We estimated propensity score-adjusted risk ratios (RRs) between first trimester exposure (any opioid analgesic and specific agents) and congenital anomalies (any anomaly, organ system anomalies, major or minor anomalies and specific anomalies). RESULTS: The prevalence of congenital anomalies was 2.8% in exposed infants and 2.0% in unexposed infants. Relative to unexposed infants, we observed elevated risks among those who were exposed for some anomaly groups, including gastrointestinal anomalies (any opioid analgesic: adjusted RR 1.46, 95% confidence interval [CI] 1.15-1.85; codeine: adjusted RR 1.53, 95% CI 1.12-2.09; tramadol: adjusted RR 2.69, 95% CI 1.34-5.38) and several specific anomalies, including ankyloglossia (any opioid: adjusted RR 1.88, 95% CI 1.30-2.72; codeine: adjusted RR 2.14, 95% CI 1.35-3.40). These findings persisted in sensitivity analyses. INTERPRETATION: Although the absolute risk of congenital anomalies was low, our findings add to accumulating data that suggest a small increased risk of some organ system anomalies and specific anomalies with first trimester exposure to opioid analgesics. These findings further quantify the potential risks associated with prenatal exposure to opioid analgesics to inform treatment choices for pain in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".