Role of ketamine in the treatment of substance use disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Substance Use Disorders (SUDs) involve diminished control, risky use, impaired social interactions, and physical dependence. Despite their global prevalence and burden, treatment options remain limited. Ketamine (KET), an NMDA receptor antagonist, may aid SUD treatment by modulating glutamatergic neurotransmission. This systematic review evaluates KET's role in SUD treatment. METHODS: This review surveyed three databases until June 2024, including 14 studies with 551 participants. RESULTS: Among the 14 studies, 6 focused on alcohol, 3 on cocaine, 4 on opioids, and 1 on cannabis. Seven studies (50 %) combined KET with psychotherapy, while seven (50 %) focused solely on KET's pharmacological effects. KET dose ranges varied from 0.11 mg/kg to 2.0 mg/kg and study primary endpoints ranged from less than a day to two years. The results of the included studies demonstrated KET's efficacy across various SUDs. In Alcohol Use Disorder (AUD), KET reduced withdrawal symptoms and benzodiazepine requirements. In Cocaine Use Disorder (CUD), KET decreased craving and increased abstinence rates. In Opioid Use Disorder (OUD), high-dose KET psychotherapy (KPT) improved abstinence and reduced craving. In Cannabis Use Disorder (CNUD), KET reduced weekly use and increased abstinence confidence. CONCLUSIONS: Conclusion: While preliminary studies suggest that KET may have short-term benefits in treating SUDs, the evidence remains limited by small sample sizes and a lack of randomized trials. Further research with larger, well-controlled studies is needed to determine optimal dosing, clarify mechanisms of action, and assess long-term efficacy and potential risks, including abuse liability, before broader clinical implementation can be recommended.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".