Neonatal Outcomes in a Medicaid Population With Opioid Dependence
Bibliographic record
Abstract
Confounding may account for the apparently improved infant outcomes after prenatal exposure to buprenorphine versus methadone. We used Massachusetts Medicaid Analytic eXtract (MAX) data to identify a cohort of opioid-dependent mother-infant pairs (2006-2011), supplemented with confounder data from an external Boston, Massachusetts, cohort (2015-2016). Associations between prenatal buprenorphine exposure versus methadone exposure and infant outcomes in the MAX cohort were adjusted for measured MAX confounders and were additionally adjusted for unmeasured confounders with bias analysis using external cohort data. A total of 477 women in MAX were treated with methadone and 543 with buprenorphine. More buprenorphine users than methadone users were white and used psychotropic medications. After adjustment for MAX confounders, risk ratios among infants exposed to buprenorphine versus those exposed to methadone were 0.45 (95% confidence interval (CI): 0.34, 0.61) for preterm birth (birth at <37 weeks) and 0.75 (95% CI: 0.51, 1.11) for low birth weight for gestational age. The mean difference in infant hospitalization was -7.35 days (95% CI: -9.16, -5.55). After further adjustment with bias analysis, the risk ratios were 0.53 (95% CI: 0.39, 0.71) for preterm birth and 1.14 (95% CI: 0.77, 1.69) for low birth weight for gestational age, and the mean difference in infant hospitalization was -3.66 days (95% CI: -5.46, -1.87). External confounder data can be used to adjust for unmeasured confounding in studies of prenatal outcomes among women on opioid agonist therapy based on administrative databases.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".