Successful Management of Gamma-hydroxybutyrate (GHB) Withdrawal Using Baclofen as a Standalone Therapy: A Case Report
Bibliographic record
Abstract
BACKGROUND: Gamma-hydroxybutyrate (GHB)-a GABA-B agonist-can lead to a use disorder, and a withdrawal syndrome similar to that of alcohol. At present, evidence is lacking for how to best manage GHB withdrawal, and often clinicians rely on alcohol withdrawal management approaches, using medications like benzodiazepines (BZD). However, BZD doses needed to control GHB withdrawal symptoms are typically much higher than those required for alcohol, posing significant safety risks. Novel approaches include the use of baclofen as an adjunct to BZD, allowing reductions in BZD requirements. While the use of baclofen as monotherapy may result in even greater risk reductions, research to support this approach is limited. CASE: We present a case of a 26-year-old female with severe GHB use disorder and history of severe withdrawal symptoms, whose withdrawal was successfully, managed using baclofen alone. CONCLUSION: In keeping with other case reports, baclofen appears to have potential to be used in the management of GHB withdrawal. Here, we presented a case of severe GHB withdrawal which was managed solely by baclofen. Clinical research is needed to evaluate baclofen's potential as a standalone treatment for GHB withdrawal.
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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 | 0.002 |
| 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 | 0.006 | 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".