A Systematic Review of the Effect of Short Term Transcranial Direct Current Stimulation Therapy in Methamphetamine Use Disorder
Bibliographic record
Abstract
Background: Methamphetamine use disorder (MUD) has become more and more common. Some studies have shown that Transcranial Direct Current stimulation (tDCS) may reduce craving when stimulating the dorsal lateral prefrontal cortex. Objectives: The aim of this systematic review was to evaluate the effect of transcranial direct current stimulation (tDCS) on MUD. Databases were searched through May 2022. Randomized Controlled Trials (RCT) and pre-post studies investigating the efficacy of tDCS in MUD were included. The Cochrane Manual of Systematic Evaluation 6.3 bias risk assessment tool was used to assess the risk of bias. For each article, where possible, we extracted the population(s), standardized mean differences (SMD), standard deviations, and other study metrics (design, year, randomization, and details) on efficacy and tolerability outcomes. We assessed each article’s quality via the GRADE assessment protocol. Results: Six studies involving 220 patients were included. All six studies included reported continuous data on craving. Results from craving favored active tDCS over sham tDCS at the end of treatment (SMD −0.58, 95% CI −0.85 to −0.30; studies = 6, participants = 220; I2 = 60%). Tolerability data showed that tDCS does not cause more tingling or itching sensation compared to sham tDCS. Conclusions: Further trials with larger sample sizes and longer durations are needed to determine whether tDCS is a valuable tool in treating MUD.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".