Consensus Statement on Pain Management for Pregnant Patients with Opioid-Use Disorder from the Society for Obstetric Anesthesia and Perinatology, Society for Maternal-Fetal Medicine, and American Society of Regional Anesthesia and Pain Medicine
Bibliographic record
Abstract
Pain management in pregnant and postpartum people with an opioid-use disorder (OUD) requires a balance between risks associated with opioid tolerance, including withdrawal or return to opioid use, considerations around social needs of the maternal-infant dyad, and the provision of adequate pain relief for the birth episode that is often characterized as the worst pain a person will experience in their lifetime. This multidisciplinary consensus statement between the Society for Obstetric Anesthesia and Perinatology (SOAP), Society for Maternal-Fetal Medicine (SMFM), and American Society of Regional Anesthesia and Pain Medicine (ASRA) provides a framework for pain management in obstetric patients with OUD. The purpose of this consensus statement is to provide practical and evidence-based recommendations and is targeted to health care providers in obstetrics and anesthesiology. The statement is focused on prenatal optimization of pain management, labor analgesia, and postvaginal delivery pain management, and postcesarean delivery pain management. Topics include a discussion of nonpharmacologic and pharmacologic options for pain management, medication management for OUD (eg, buprenorphine, methadone), considerations regarding urine drug testing, and other social aspects of care for maternal-infant dyads, as well as a review of current practices. The authors provide evidence-based recommendations to optimize pain management while reducing risks and complications associated with OUD in the peripartum period. Ultimately, this multidisciplinary consensus statement provides practical and concise clinical guidance to optimize pain management for people with OUD in the context of pregnancy to improve maternal and perinatal outcomes.
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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 | 0.037 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".