Non-invasive neuromodulation of dorsolateral prefrontal cortex to reduce craving in alcohol use disorder: A meta-analysis
Bibliographic record
Abstract
While several pharmacological and behavioral treatments are available for alcohol use disorder (AUD), they may not be effective for all patients. The aim of this systematic review and meta-analysis was to evaluate the efficacy and safety of rTMS and tDCS for craving in AUD. EMBASE, Cochrane Library, PsycINFO, and PubMed databases were searched for original, peer-reviewed research articles in the English language published between January 2000 and January 2022. Randomized controlled trials (RCTs) reporting changes in alcohol craving among patients with AUD were selected. Random-effects meta-analysis was employed to pool data. Changes in alcohol craving were extracted from 15 RCTs. Six studies assessed the efficacy of rTMS while nine studies examined tDCS. Results demonstrated that in comparison to sham stimulation, active rTMS to the DLPFC yields small but significant reductions in alcohol craving (standardized mean difference [SMD] = -0.27, p = .03). However, DLPFC stimulation via tDCS was not superior to sham stimulation in producing changes in alcohol craving (SMD = -0.08, p = .59). Our meta-analysis suggests that rTMS may be superior to tDCS in reducing alcohol craving in patients with AUD. However, additional research is needed to identify optimal stimulation parameters for both non-invasive neuromodulatory techniques in AUD.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".