The Use of Repetitive Transcranial Magnetic Stimulations for the Treatment of Post-Traumatic Stress Disorder: A Scoping Review
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive procedure in which brain neural activity is stimulated by the direct application of a magnetic field to the scalp. Despite its wide and continuous usage for the management of psychiatric disorders, the use of rTMS for post-traumatic stress disorder (PTSD) is not well established and evaluated by researchers. This scoping review seeks to explore the relevant literature available regarding the use of rTMS as a mode of treatment for PTSD, to map evidence in support of the use of rTMS for PTSD, and recommendations on future clinical and research work. Five databases were searched (MEDLINE, CINAHL, Psych INFO, SCOPUS, and EMBASE) to identify empirical studies and randomized controlled trials aimed at the treatment of PTSD with rTMS. A total of 10 studies were eligible for this review. The search results are up to date as of the date of the electronic data search of 20 December 2020. The frequencies applied in the studies ranged from low (1 Hz) to high (10 Hz) at different thresholds. Nine reported significant positive outcomes and PTSD symptoms improvement. rTMS was reported as well tolerated with no significant side effects. The application of rTMS for PTSD looks promising despite the diversity in terms of its outcomes and its clinical significance. Studies with well-defined stimulation parameters need to be conducted in the future.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".