Comprehensive bibliometric analysis of transcranial magnetic stimulation-based depression treatment from 2003 to 2022: Research hotspots and trends
Bibliographic record
Abstract
BACKGROUND: Depression has become a global public health problem. In recent years, transcranial magnetic stimulation (TMS) has gained considerable attention as a non-invasive treatment for depression. AIM: To investigate the research hotspots and trends in the field of TMS-based depression treatment from a bibliometric perspective. METHODS: Using the Web of Science Core Collection, articles published between 2003 and 2022 on TMS-based depression treatment were retrieved from the science citation index expanded. The publication trends and research hotspots were analyzed using VOSviewer, CiteSpace, and the bibliometric online analysis platform. Regression analysis was performed using Microsoft Excel 2021 to predict publication growth trends. RESULTS: We identified a total of 702 articles on TMS-based depression treatment with a predominance of clinical studies. Analysis of collaborative networks showed that the United States, the University of Toronto, and Daskalakis ZJ were identified as the most impactful country, institution, and researcher, respectively. In keyword burst analysis, it was found that theta burst stimulation (TBS), functional connectivity, and frequency were the most recent research hotspots. CONCLUSION: TMS provides a novel therapeutic option for patients with treatment-resistant depression. Neuroimaging technology enables more precise TMS treatment, while the novel TMS modality, TBS, enhances both therapeutic efficacy and patient experience in TMS-based depression treatment. The integration of neuroimaging techniques with TBS represents a promising research direction for advancing TMS-based depression treatment. This study presents systematic information and recommendations to guide future research on TMS-based depression treatment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.091 | 0.188 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".