Prenatal Opioid Analgesics and the Risk of Adverse Birth Outcomes
Why this work is in the frame
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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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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 | 0.017 |
| 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 it