Opioid Agonist to Buprenorphine Cross-titration During Pregnancy: A Case Report
Bibliographic record
Abstract
BACKGROUND: We present a case report of a first-trimester pregnant individual with chronic pain on chronic opioid therapy who successfully cross-titrated from full-μ agonist opioid to buprenorphine without causing significant withdrawal symptoms. CASE PRESENTATION: A 37-year-old gravida 1, para 0 woman with chronic pain on opioid therapy successfully completed a 6-week cross-titration from 120 morphine equivalent dose to buprenorphine in her first trimester without affecting pain scores, functional capacity, withdrawal symptoms except for mild nausea and insomnia, or adverse perinatal outcomes. After increasing her buprenorphine in the second trimester, at 38 weeks, she bore a healthy neonate without eliciting signs of neonatal abstinence syndrome while on a stable buprenorphine dose. CONCLUSIONS: The American College of Obstetricians and Gynecologists and the American Society of Addiction Medicine agree that pregnant patients with chronic pain should avoid or minimize opioids. For patients on chronic opioid therapy unable to minimize opioid use during pregnancy, it is unclear whether to continue their chronic opioid therapy or transition to other medications, including buprenorphine. This case demonstrated how one pregnant person with chronic pain on opioid therapy but not meeting diagnostic criteria for opioid use disorder safely transitioned from full-μ agonist opioids to buprenorphine without precipitating withdrawal or adverse perinatal outcomes. Cross-titration could be similarly performed for a pregnant patient with untreated opioid use disorder. In addition, the used cross-titration schedule and the rationale are provided.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".