Prescription psychostimulants for the treatment of amphetamine‐type stimulant use disorder: A systematic review and meta‐analysis of randomized placebo‐controlled trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is currently no standard of care for pharmacological treatment of amphetamine-type stimulant (ATS) use disorder (ATSUD). This systematic review with meta-analysis (PROSPERO CRD42022354492) aimed to pool results from randomized placebo-controlled trials (RCTs) to evaluate efficacy and safety of prescription psychostimulants (PPs) for ATSUD. METHODS: Major indexing sources and trial registries were searched to include records published before 29 August 2022. Eligible studies were RCTs evaluating efficacy and safety of PPs for ATSUD. Risk of bias (RoB) was assessed using the Cochrane RoB 2 tool. Risk ratio (RR) and risk difference were calculated for random-effect meta-analysis of dichotomous variables. Mean difference and standardized mean difference (SMD) were calculated for random-effect meta-analysis of continuous variables. RESULTS: Ten RCTs (n = 561 participants) were included in the meta-analysis. Trials studied methylphenidate (n = 7), with daily doses of 54-180 mg, and dextroamphetamine (n = 3), with daily doses of 60-110 mg, for 2-24 weeks. PPs significantly decreased end-point craving [SMD -0.29; 95% confidence interval (CI) = -0.55, -0.03], while such a decrease did not reach statistical significance for ATS use, as evaluated by urine analysis (UA) (RR = 0.93; 95% CI = 0.85-1.01). No effect was observed for self-reported ATS use, retention in treatment, dropout following adverse events, early-stage craving, withdrawal and depressive symptoms. In a sensitivity analysis, treatment was associated with a significant reduction in UA positive for ATS (RR = 0.89; 95% CI = 0.79-0.99) after removing studies with a high risk of bias. In subgroup analyses, methylphenidate and high doses of PPs were negatively associated with ATS use by UA, while higher doses of PPs and treatment duration (≥ 20 weeks) were positively associated with longer retention. CONCLUSIONS: Among individuals with amphetamine-type stimulant use disorder, treatment with prescription psychostimulants may decrease ATS use and craving. While effect size is limited, it may increase with a higher dosage of medications.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".