Prenatal Opioid Use Disorder and the Risk of Congenital Anomalies in Offspring: A Population‐Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| 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) | 0.000 | 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 it