Distinct Trajectories of Prescription Opioid Exposure in Pregnancy and Risk of Adverse Birth Outcomes
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to identify distinct trajectories of prescription opioid exposure in pregnancy-encompassing both medication for opioid use disorder (MOUD) and opioid analgesics-and explore their associations with birth outcomes. METHODS: Trajectories were identified using latent class analysis among Wisconsin Medicaid-insured live births 2011-2019. Logistic regression estimated associations between these trajectories and neonatal opioid withdrawal syndrome (NOWS), small for gestational age, preterm birth, birth weight, and gestational age. RESULTS: Of 138,123 births, 27,293 (19.8%) had prenatal opioid exposure. Five trajectory classes were identified: (1) stable MOUD treatment (5.8%), (2) inconsistent MOUD treatment (3.9%), (3) chronic analgesic use (4.2%), (4) intermittent analgesic use (7.8%), and (5) low-level use of MOUD and analgesics (78.3%). NOWS incidence per 1000 infants was 667 for class 1 (adjusted odds ratio [aOR]: 21.74, 95% confidence interval [CI]: 17.89, 26.41), 570 for class 2 (aOR: 15.35, 95% CI: 12.49, 18.87), 235 for class 3 (aOR: 19.42, 95% CI: 15.93, 23.68), 67 for class 4 (aOR: 6.23, 95% CI: 4.99, 7.76), and 12 for class 5 (aOR: 1.73, 95% CI: 1.47, 2.02). Classes 1-4 had elevated risk of small for gestational age, preterm birth, lower birth weight, and shorter gestational age, with no significant differences among these classes. Among individuals with opioid use disorder, stable MOUD treatment was associated with higher birth weights and longer gestational ages compared to inconsistent treatment, despite higher odds of NOWS. CONCLUSIONS: Early initiation and consistent MOUD treatment may improve birth weight and gestational age. For pregnant individuals with opioid use disorder using chronic analgesics, transition to MOUD may promote birth outcomes.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".