Transcranial magnetic stimulation for the rehabilitation of patients with addiction: current status and future prospects
Bibliographic record
Abstract
INTRODUCTION: Substance use disorders (SUDs) are severe conditions that remain extremely challenging to treat in clinical practice. With high rates of non-response to current treatment options and several SUDs with no approved interventions, novel therapies are needed. Repetitive transcranial magnetic stimulation (rTMS) can non-invasively modulate the neurocircuitry of brain-based disorders, and investigation into its therapeutic potential for SUDs is growing rapidly. AREAS COVERED: In this review, we summarize the clinical research to date evaluating its safety and efficacy for various SUDs. We highlight the investigations comparing different stimulation parameters to present our current understanding on optimal stimulation parameters. Additionally, we cover key research avenues in the use of neuroimaging to guide treatment, cue-induction paradigms, and adjunctive or combination treatments that may optimize outcomes. EXPERT OPINION: Evidence of rTMS as an effective treatment for certain SUDs has emerged and is preliminary for others. There are a growing number of studies showing benefit and meta-analyses suggesting that rTMS can significantly reduce substance craving and consumption. However, the optimal approach has not been determined, and there is a great deal of heterogeneity in rTMS protocols and mixed outcomes. Further research into strategies for enhancing precision will be crucial in moving the field forward.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".