Global trends and research hotspots in the treatment of mental disorders with transcranial magnetic stimulation: a bibliometric analysis
Bibliographic record
Abstract
Background: Transcranial Magnetic Stimulation (TMS) is increasingly utilized in the treatment of mental disorders (MD). The exploration and expanding application of various new TMS mode have significantly propelled the advancement of related clinical research. Methods: We reviewed research published in the Science Citation Index Expanded of Web of Science Core Collection database. Using Citespace 6.1, Vosviewer 1.6.20, and Scimago Graphica 1.0.38 software, we conducted a comprehensive visual analysis of TMS on MD from multiple dimensions, including influential countries/regions, institutions, authors, and high-frequency keywords and burst keywords. Results: A total of 611 papers between 1996 and 2023 were identified. Recently, the application of TMS on MD have gained increasing recognition. The USA leads in research publications in this field, followed by Germany and China. Institutionally, the University of Toronto in Canada ranks first (n=48); Professor Zafiris J. Daskalakis from the University of California tops among individual researchers (n=24). Cluster analysis of keywords reveal four representative clusters, demonstrating shifts in research focus and direction over time. Current hotspots focus on exploring the effectiveness of different TMS modes and stimulation targets in treating severe depression, obsessive-compulsive disorder, and schizophrenia. Analysis of burst keywords indicated that the latest research are the feasibility and safety of various emerging TMS stimulation mode for treating refractory depression, obsessive-compulsive disorder, negative symptoms of schizophrenia. Conclusions: Our study provides valuable insights into the current hotspots and emerging trends of TMS in the treatment of MD, providing a direction for future research to consider.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.165 | 0.217 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".