Current Status of Transcranial Magnetic Stimulation for Treating Depression: A Visualization and Bibliometric Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Background: Depression is the most disabling and prevalent psychiatric disorder; transcranial magnetic stimulation (TMS) is widely used in the treatment of depression because of its remarkable efficacy. OBJECTIVE Objective: To investigate the current status, hotspots and frontiers of this research field, and advance the research on TMS for treating depression, this paper provides a visualization and bibliometric analysis of studies related to TMS for depression. METHODS Methods: Literature related to TMS for depression was searched based on the Web of science core database from database creation to November 19, 2023. Cite Space 6.2.R4 and VOS viewer 1.6.20 were used to analyze the relevant literature in terms of annual publications, authors, institutions and international collaborations, co-cited literature, co-cited authors, co-cited journals, and keywords. RESULTS Results: A total of 4218 papers were included. The overall trend of the number of publications in this research area is increasing year by year. Research fervor is expected to continue to increase. The United States is in the top position both in terms of the number of publications and centrality. Although Canada ranks third in terms of the number of articles published, its centrality is not high. Based on the keyword co-occurrence analysis, the research hotspots in this field were clarified as efficacy, dorsolateral prefrontal cortex, prefrontal cortex, motor cortex and so on. In recent years, the keywords that have burst out and have continued until now are the treatment effectiveness, reliability, frequency, and theta burst treatment modality of TMS. These keywords may become hot spots for future research. CONCLUSIONS Conclusion: Our findings suggested that studies related to the field of TMS depression are increasingly emphasized by researchers, and the United States is an international leader in this research area. In the future, cooperation between countries should be strengthened. Meanwhile, it is important to use various imaging-assisted localization tools to carry out multi-center and large-sample clinical studies of individualized treatment parameters for TMS.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.078 | 0.100 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".