High-frequency rTMS as a first-line treatment for gambling disorder – A case report
Bibliographic record
Abstract
Gambling Disorder is a chronic and debilitating condition, often associated with impulsivity, mood disturbances, and significant social and financial consequences. Despite various pharmacological and psychotherapeutic approaches, standardized treatment is lacking. Repetitive transcranial magnetic stimulation (rTMS) has recently emerged as a potential neuromodulatory intervention for addictions. We report the case of a male patient in his sixties with severe Gambling Disorder who underwent TMS treatment with high-frequency rTMS targeting the left dorsolateral prefrontal cortex as a first-line therapy. The patient completed a six-week protocol. Clinical assessment was conducted at baseline, after six weeks, and after three months using multiple validated scales, including the South Oaks Gambling Screen, the Visual Analog Scale for craving, the Barratt Impulsiveness Scale, the Hamilton Anxiety and Depression Scales and the Canadian Problem Gambling Index. The patient's South Oaks Gambling Screen score decreased from 20.5 to 2.0, and craving assessed via Visual Analog Scale reduced from 85 to 15 over three months. Reductions in anxiety, depression, and impulsivity were also observed, along with improvements in emotional stability and extroversion. Cognitive performance remained stable, and no adverse effects were reported throughout the treatment. This case highlights the potential of rTMS as a safe and effective intervention for Gambling Disorder, particularly in patients who are not willing to undergo a psychotherapy or pharmacological treatment. The progressive reduction in craving highlights the role of high rTMS in regulating compulsive behaviour and impulsive control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".