Treatment of Gamma Hydroxybutyrate Withdrawal in a Pregnant Female: A Case Report
Bibliographic record
Abstract
BACKGROUND: Gamma hydroxybutyrate (GHB) is used illicitly for its sedative hypnotic effects, and those who take it regularly are at risk of developing a substance use disorder. Withdrawal from GHB can include severe symptoms that may require medical management. For GHB use and withdrawal during pregnancy, there are no evidence- or practice-based guidelines to follow, and there is only minimal research literature. CASE SUMMARY: We present the case of a 32-year-old woman, G1P0 at 29 weeks and 6 days of gestation, admitted to the perinatal unit at a tertiary hospital for GHB withdrawal management and stabilization. GHB withdrawal was managed with a combination of baclofen and diazepam. We report the dosing and tapering of these medications throughout her 14-day admission. Withdrawal symptoms were well managed with this medication protocol, and she did not experience any features of complicated withdrawal. The patient later presented to hospital in preterm labor and precipitously delivered a healthy, preterm infant male at 34 weeks and 5 days of gestation. At 7 months postpartum, the patient continued to engage with perinatal addiction service, reported no use of GHB since her admission, and was parenting her healthy son. CLINICAL SIGNIFICANCE: There is a paucity of guidelines for managing GHB withdrawal in pregnancy. This case demonstrates good clinical outcomes administering a short-term combination of diazepam and baclofen during the third trimester of pregnancy. This case helps to fill a gap in the literature and may inform future research or clinical decision-making in similar situations.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| 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".