Clinical efficacy of deep transcranial magnetic stimulation (dTMS) in psychiatric and cognitive disorders: A systematic review
Bibliographic record
Abstract
Deep transcranial magnetic stimulation (dTMS) has gained attention as an enhanced form of traditional TMS, targeting broader and deeper regions of the brain. However, a fulsome synthesis of dTMS efficacy across psychiatric and cognitive disorders using sham-controlled trials is lacking. We systematically reviewed 28 clinical trials comparing active dTMS to a sham/controlled condition to characterize dTMS efficacy across diverse psychiatric and cognitive disorders. A comprehensive search of APA PsycINFO, Cochrane, Embase, Medline, and PubMed databases was conducted. Predominant evidence supports dTMS efficacy in patients with obsessive-compulsive disorder (OCD; n = 2), substance use disorders (SUDs; n = 8), and in those experiencing depressive episodes with major depressive disorder (MDD) or bipolar disorder (BD; n = 6). However, the clinical efficacy of dTMS in psychiatric disorders characterized by hyperactivity or hyperarousal (i.e., attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and schizophrenia) was heterogeneous. Common side effects included headaches and pain/discomfort, with rare but serious adverse events such as seizures and suicidal ideation/attempts. Risk of bias ratings indicated a collectively low risk according to the Grading of Recommendations, Assessment, Development, and Evaluations checklist (Meader et al., 2014). Literature suggests promise for dTMS as a beneficial alternative or add-on treatment for patients who do not respond well to traditional treatment, particularly for depressive episodes, OCD, and SUDs. Mixed evidence and limited clinical trials for other psychiatric and cognitive disorders suggest more extensive research is warranted. Future research should examine the durability of dTMS interventions and identify moderators of clinical efficacy.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".