Use of Injectable Opioid Agonist Therapy in a In-Patient Setting for a Pregnant Patient With Opioid Use Disorder: A Case Report
Bibliographic record
Abstract
BACKGROUND: In the era of highly potent illicit opioids, such as fentanyl and carfentanil, injectable opioid agonist treatment (iOAT) is an effective treatment for those with severe and treatment-refractory opioid use disorder. Untreated opioid use disorder in pregnancy can lead to maternal and neonatal morbidity and mortality. There are currently limited reports on the use of iOAT in pregnant women. The in-patient setting may provide an opportunity to pregnant women for stabilization with iOAT where first line therapies have been ineffective. CASE SUMMARY: We report a case of a pregnant individual who engaged in daily intravenous fentanyl who was admitted to the hospital at 29 weeks gestation for stabilization with iOAT, methadone, and slow-release oral morphine. Before admission, she endured 6 opioid overdoses in her pregnancy and continued to use illicit intravenous opioids in the community despite high dose methadone combined with slow-release oral morphine. Her withdrawal symptoms and cravings were ameliorated with hydromorphone 90 mg IM/IV BID, methadone 135 mg daily, and morphine sulfate sustained release 600 mg daily. With this regimen, she was able to reduce her intravenous fentanyl use to a single episode during her hospitalization. She completed her pregnancy in hospital, delivering a full-term live infant after receiving comprehensive prenatal care. DISCUSSION: This case report highlights iOAT as an option during pregnancy and describes the in-patient setting as appropriate to retain high-risk patients in care. This approach may benefit those who are refractory to standard opioid agonist treatment, the numbers of whom may be rising as tolerance to the illicit supply increases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".