Treatment of stimulant use disorder: A systematic review of reviews
Bibliographic record
Abstract
AIMS: Stimulant use disorder contributes to a substantial worldwide burden of disease, although evidence-based treatment options are limited. This systematic review of reviews aims to: (i) synthesize the available evidence on both psychosocial and pharmacological interventions for the treatment of stimulant use disorder; (ii) identify the most effective therapies to guide clinical practice, and (iii) highlight gaps for future study. METHODS: A systematic database search was conducted to identify systematic reviews and meta-analyses. Eligible studies were those that followed standard systematic review methodology and assessed randomized controlled trials focused on the efficacy of interventions for stimulant use disorder. Articles were critically appraised using an assessment tool adapted from Palmeteer et al. and categorized for quality as 'core' or 'supplementary' reviews. Evidence from the included reviews were further synthesized according to pharmacological or non-pharmacological management themes. RESULTS: Of 476 identified records, 29 systematic reviews examining eleven intervention modalities were included. The interventions identified include: contingency management, cognitive behavioural therapy, acupuncture, antidepressants, dopamine agonists, antipsychotics, anticonvulsants, disulfiram, opioid agonists, N-Acetylcysteine, and psychostimulants. There was sufficient evidence to support the efficacy of contingency management programs for treatment of stimulant use disorder. Psychostimulants, n-acetylcysteine, opioid agonist therapy, disulfiram and antidepressant pharmacological interventions were found to have insufficient evidence to support or discount their use. Results of this review do not support the use of all other treatment options. CONCLUSIONS: The results of this review supports the use of contingency management interventions for the treatment of stimulant use disorder. Although evidence to date is insufficient to support the clinical use of psychostimulants, our results demonstrate potential for future research in this area. Given the urgent need for effective pharmacological treatments for stimulant use disorder, high-quality primary research focused on the role of psychostimulant medications for the treatment of stimulant use disorder is needed.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".