Safety and Efficacy of Buprenorphine-Naloxone in Pregnancy: A Systematic Review of the Literature
Bibliographic record
Abstract
The prevalence of opioid use among pregnant people has been increasing over the past few decades, with a parallel increase in the rate of neonatal abstinence syndrome. Opioid agonist treatment (OAT) including methadone and buprenorphine is the recommended management method for opioid use disorders during pregnancy. Methadone has been extensively studied during pregnancy; however, buprenorphine was introduced in the early 2000s with limited data on the use of different preparations during pregnancy. Buprenorphine-naloxone has been incorporated into routine practice; however, only a few studies have investigated the use of this medication during pregnancy. To determine the safety and efficacy of this medication, we conducted a systematic review of maternal and neonatal outcomes among buprenorphine-naloxone-exposed pregnancies. The primary outcomes of interest were birth parameters, congenital anomalies, and severity of neonatal abstinence syndrome. Secondary maternal outcomes included the OAT dose and substance use at delivery. Seven studies met the inclusion criteria. Buprenorphine-naloxone doses ranged between 8 and 20 mg, and there was an associated reduction of opioid use during pregnancy. There were no significant differences in gestational age at delivery, birth parameters, or prevalence of congenital anomalies between buprenorphine-naloxone-exposed neonates and those exposed to methadone, buprenorphine monotherapy, illicit opioids, or no opioids. In studies comparing buprenorphine-naloxone to methadone, there were reduced rates of neonatal abstinence syndrome requiring pharmacotherapy. These studies demonstrate that buprenorphine-naloxone is a safe and effective opioid agonist treatment for pregnant people with OUD. Further large-scale, prospective data collection is required to confirm these findings. Patients and clinicians may be reassured about the use of buprenorphine-naloxone during pregnancy.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".