Buprenorphine treatment of opioid‐dependent pregnant women: a comprehensive review
Bibliographic record
Abstract
AIMS: This paper reviews the published literature regarding outcomes following maternal treatment with buprenorphine in five areas: maternal efficacy, fetal effects, neonatal effects, effects on breast milk and longer-term developmental effects. METHODS: Within each outcome area, findings are summarized first for the three randomized clinical trials and then for the 44 non-randomized studies (i.e. prospective studies, case reports and series and retrospective chart reviews), only 28 of which involve independent samples. RESULTS: Results indicate that maternal treatment with buprenorphine has comparable efficacy to methadone, although difficulties may exist with current buprenorphine induction methods. The available fetal data suggest buprenorphine results in less physiological suppression of fetal heart rate and movements than methadone. Regarding neonatal effects, perhaps the single definitive conclusion is that prenatal buprenorphine treatment results in a clinically significant less severe neonatal abstinence syndrome (NAS) than treatment with methadone. The limited research suggests that, like methadone, buprenorphine is compatible with breastfeeding. Data available thus far suggest that there are no deleterious effects of in utero buprenorphine exposure on infant development. CONCLUSIONS: While buprenorphine produces a less severe neonatal abstinence syndrome than methadone, both methadone and buprenorphine are important parts of a complete comprehensive treatment approach for opioid-dependent pregnant women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".