Maternal prepregnancy surgery and risk of neonatal abstinence syndrome in future newborns: a longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Neonatal abstinence syndrome is increasingly prevalent, and may be related to opioid use disorders caused by postoperative prescriptions for pain control. We assessed the association of maternal prepregnancy surgery with risk of neonatal abstinence syndrome from opioid use disorders in future pregnancies. METHODS: We conducted a longitudinal retrospective cohort study of 2 182 365 deliveries in Quebec, Canada, between 1989 and 2016. The main exposure was maternal prepregnancy surgery. The main outcome measure was neonatal abstinence syndrome in offspring. We adjusted associations for maternal comorbidity and pregnancy characteristics using log-binomial regression models. RESULTS: The prevalence of neonatal abstinence syndrome in the cohort was 10.7 per 10 000 births. Compared with no surgery, prepregnancy surgery was associated with a risk ratio (RR) of neonatal abstinence syndrome of 1.63 (95% confidence interval [CI] 1.49-1.78). Risk was greater for 3 or more prepregnancy surgeries (RR 2.34, 95% CI 2.07-2.63) and age < 15 years at first surgery (1 surgery: RR 2.08, 95% CI 1.71-2.54; 2 or more surgeries: RR 2.79, 95% CI 2.32-3.37). Nearly all surgical specialties increased the risk of neonatal abstinence syndrome, but associations were strongest for cardiothoracic surgery (RR 4.45, 95% CI 2.87-6.91), neurosurgery (RR 3.00, 95% CI 1.56-5.77) and urologic surgery (RR 3.03, 95% CI 2.16-4.26). INTERPRETATION: Prepregnancy surgery is associated with the risk of neonatal abstinence syndrome in future pregnancies. Prescription opioids for postsurgical pain may result in opioid use disorders during future pregnancies, inadvertently increasing the risk of neonatal abstinence syndrome in offspring.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".