Prenatal Opioid Analgesics and the Risk of Adverse Birth Outcomes
Bibliographic record
Abstract
BACKGROUND: It is unclear whether confounding accounts for the increased risk of preterm birth and small for gestational age (SGA) birth in opioid analgesic exposed pregnancies. METHODS: Using universal coverage health data for Ontario, we assembled a cohort of mother-infant pairs without opioid use disorder (627,172 pregnancies and 509,522 women). We estimated risk ratios (RRs) between opioid analgesics and preterm birth, SGA birth, and stillbirth; neonatal abstinence syndrome was a secondary outcome. We used high-dimensional propensity scores and sensitivity analyses for confounding adjustment. RESULTS: 4% of pairs were exposed, mainly to codeine (2%), morphine (1%), and oxycodone (1%). Compared with unexposed, the adjusted risk of preterm birth was higher with any (1.3, 95% confidence interval [CI] = 1.2, 1.3), first- (RR: 1.2, 95% CI = 1.2, 1.3), and second-trimester (RR: 1.3, 95% CI = 1.2, 1.4) opioid analgesic exposure. Preterm birth risk was higher for first- and second-trimester codeine, morphine, and oxycodone exposure, and for third-trimester morphine. There was a small increase in SGA with first-trimester exposure to any opioid analgesic or to codeine. Exposed pregnancies had an elevated stillbirth risk with any (RR: 1.6, 95% CI = 1.4, 1.8), first- and second-trimester exposure. Few infants had neonatal abstinence syndrome (N = 143); the risk was higher in exposed (RR: 3.6, 95% CI = 2.1, 6.0). In sensitivity analyses of unmeasured confounding, an elevated risk in exposed pregnancies persisted for preterm birth but not SGA. CONCLUSIONS: Opioid analgesic-exposed pregnancies had a small increased risk of preterm birth and possibly stillbirth after accounting for confounding by indication and sociodemographic factors.
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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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".