Buprenorphine-naloxone use in pregnancy for treatment of opioid dependence: Retrospective cohort study of 30 patients
Bibliographic record
Abstract
OBJECTIVE: To examine the maternal course and neonatal outcomes for women using buprenorphine-naloxone for opioid dependence in pregnancy. DESIGN: Retrospective cohort study comparing outcomes for the group of pregnant patients exposed to buprenorphine-naloxone with outcomes for those exposed to other narcotics and those not exposed to narcotics. SETTING: Northwestern Ontario obstetric program. PARTICIPANTS: A total of 640 births in an 18-month period from July 1, 2013, to January 1, 2015. MAIN OUTCOME MEASURES: Maternal outcomes included route and time of delivery, medical and surgical complications, out-of hospital deliveries, change in illicit drug use, and length of stay. Neonatal outcomes included stillbirths, incidence and severity of neonatal abstinence syndrome, birth weight, gestational age, Apgar scores, and incidence of congenital abnormalities. RESULTS: Thirty pregnant women used buprenorphine-naloxone for a mean (SD) of 18.8 (11.2) weeks; an additional 134 patients were exposed to other opioids; 476 pregnant women were not exposed to opioids. Maternal and neonatal outcomes were similar among the 3 groups, other than the expected clinically insignificant lower birth weights among those exposed to opioids other than buprenorphine-naloxone. CONCLUSION: Buprenorphine-naloxone appears to be safe for use in pregnancy for opioid-dependence substitution therapy. Transferring a pregnant patient to another opioid agonist that has greater abuse potential might not be necessary.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".