Perinatal Injectable Opioid Agonist Therapy (iOAT) Administration: A Case Series
Bibliographic record
Abstract
OBJECTIVES: Untreated opioid use disorder (OUD) in pregnancy may lead to adverse outcomes for the individual and fetus. Injectable opioid agonist therapy (iOAT) is the highest intensity treatment for severe refractory OUD currently available; however, research on perinatal administration is limited. We present the first known case series of 13 pregnant or postpartum participants who received intravenous hydromorphone while admitted to the Families in Recovery (FIR) unit, an in-patient perinatal stabilization unit in Canada. METHODS: Patients who received iOAT at FIR between 2019 and 2022 were invited to participate. Prospectively enrolled participants completed a self-report sociodemographics and exposures survey. Medical/social backgrounds of participants at admission, iOAT and other opioid agonist therapy administration, and health/social outcomes of mother and infant at discharge were collected on all participants via retrospective maternal and infant medical chart review. RESULTS: Participants initiated iOAT while pregnant (n = 5) or postpartum (n = 8) and received iOAT for 23 days on average. At discharge, 8 participants underwent planned transition to community with infant in their care and a discharge plan including outpatient prescriptions, housing arrangements, follow-up appointments, and supportive programming. All infants received oral morphine after delivery and were discharged in good health. CONCLUSIONS: This is the first known case series of iOAT administration in the peripartum. The cases illustrate iOAT as an option that can achieve OUD stabilization in perinatal individuals to support patient engagement and retention in care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".