Prenatal Opioid Use Disorder and the Risk of Congenital Anomalies in Offspring: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: To examine whether prenatal opioid use disorder (OUD) diagnosis is associated with the risk of congenital anomalies (CAs) in offspring. METHODS: We conducted a population-based study of mother-newborn dyads comprising. 4143 761 births delivered in Canada from 2006 to 2021. We used robust Poisson regression to examine the association between prenatal OUD diagnosis and risk of non-chromosomal CAs, adjusted for maternal age, parity, multiple gestation, co-morbidities (including mental health disorders, chronic illnesses and other substance use disorders), and infant sex. RESULTS: We identified a total of 21, 638 births to persons who were diagnosed with prenatal OUD and 65, 992 (159.3 per 10,000) newborns with CAs. The overall risk of CAs was 2.3 times higher in infants born to birthing persons with a diagnosis of OUD (95% CI 2.2, 2.5). Compared to those without OUD diagnoses, births to persons with a diagnosis of OUD had a higher risk of specific types of congenital microcephaly (aRR 5.2, 95% CI 4.1, 6.6), cleft palate (RR 4.8, 95% CI 3.7, 6.1), pulmonary valve atresia with intact ventricular septum (aRR 2.7, 95% CI 1.1, 6.7), and atrial septal defect (aRR 3.1, 95% CI 2.8, 3.5), among others. In particular, infants born to those with an OUD diagnosis had a 1.8 (95% CI 1.4, 2.3)-fold increased risk of having severe congenital heart disease. CONCLUSION: Our findings suggest an association between prenatal OUD diagnosis and certain CAs in the offspring. Future research is necessary to better understand the role of socio-demographic factors on these associations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".