Buprenorphine Compared with Methadone in Pregnancy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Illicit opioid use in pregnancy is associated with adverse maternal, neonatal, and childhood outcomes. Opioid substitution is recommended, but whether methadone or buprenorphine is the optimal agent remains unclear. We searched EMBASE, PubMed, Web of Science, Scopus, Open Gray, CINAHL and the Cochrane Central Registry of Controlled Trials (CENTRAL) from inception to April 2020 for randomized controlled trials (RCTs) and cohort studies comparing methadone and buprenorphine treatment for opioid-using mothers. Included studies assessed maternal and or neonatal outcomes. We used random-effects meta-analyses to estimate summary measures for outcomes and report these separately for RCTs and cohort studies. Of 408 abstracts screened, 20 papers were included (4 RCTs, 16 cohort, 223 and 7028 participants respectively). All RCTs (4/4) had a high risk of bias and median (IQR) Newcastle Ottawa Scale for cohort studies was 7.5 (6–9). In both RCTs and cohort studies, buprenorphine was associated with; greater offspring birth weight (weighted mean difference [WMD] 343 g (95% CI: 40–645 g) in RCT and 184 g (95% CI: 121–247 g) in cohort studies); body length at birth (WMD 2.28 cm (95% CI: 1.06–3.49 cm) in RCTs and 0.65 cm (95% CI: 0.31–0.98 cm) in cohort studies); and reduced risk of prematurity (risk ratio [RR] 0.41 (95% CI: 0.18–0.93) in RCTs and 0.63 [95% CI: 0.53–0.75] in cohort studies) when compared to methadone. All other clinical outcomes were comparable. Compared to methadone, buprenorphine was consistently associated with improved birthweight and gestational age, however given potential biases, results should be interpreted with caution.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".