Methadone Versus Buprenorphine for Opioid Use Dependence and Risk of Neonatal Abstinence Syndrome
Bibliographic record
Abstract
BACKGROUND: Our objective was to estimate the association between methadone and neonatal abstinence syndrome compared with buprenorphine using a probabilistic bias analysis to account for unmeasured confounding by severity of addiction. METHODS: We used a cohort of live-born infants exposed in utero to methadone or buprenorphine for maternal opioid maintenance therapy at Magee-Womens Hospital in Pittsburgh, PA, from 2013 to 2015 (n = 716). We determined exposure and outcome status using pharmacy billing claims. We used log-binomial regression models to assess association of treatment with neonatal abstinence syndrome after adjusting for parity, maternal race, age, delivery year, employment, hepatitis c, smoking, marital, and insurance status. We implemented probabilistic bias analysis, informed by an internal validation study, to assess the impact of unmeasured confounding by severity of addiction. RESULTS: Infants exposed to methadone in utero were more likely to experience neonatal abstinence syndrome compared with those exposed to buprenorphine (RR, 1.3; 95% CI, 1.2, 1.5). After adjustment, infants exposed to methadone were more likely (adjusted RR, 1.3; 95% CI, 1.1, 1.5) than infants exposed to buprenorphine to have the syndrome. In the validation cohort (n = 200), severe addiction was more common in methadone- versus buprenorphine-exposed deliveries (77% vs. 32%). However, adjustment for severe addiction in the bias analysis only slightly attenuated the association (RR, 1.2; 95% CI, 1.0, 1.4), supporting conventional analysis. CONCLUSIONS: Methadone is associated with increased risk of neonatal abstinence syndrome compared with buprenorphine in infants exposed in utero. This association is subject to minimal bias due to unmeasured confounding by severity of addiction.
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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 | 0.002 | 0.048 |
| 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".