Transcranial magnetic stimulation in neurological rehabilitation: A bibliometric mapping of 34 years of literature
Bibliographic record
Abstract
Aim: This study aims to analyze the global research landscape on transcranial magnetic stimulation (TMS) in neurological rehabilitation using bibliometric methods.It investigates publication trends, key authors and institutions, international collaboration patterns, and disease-specific research distribution to highlight current progress and research gaps.Material and Methods: A total of 2,245 English-language publications from 1991 to 2025 were retrieved from the Web of Science Core Collection (WoSCC) using a specific search strategy targeting TMS, rehabilitation, and neurological disorders.Bibliometric and scientometric analyses were conducted using Biblioshiny and VOSviewer.Analyses included productivity trends, citation metrics, keyword co-occurrence, thematic clustering, and collaboration networks.Results: The number of TMS-related publications in neurorehabilitation showed a sharp increase after 2015, with an average annual growth rate of 13.8%.Stroke, spinal cord injury, Parkinson's disease, and multiple sclerosis were the most studied conditions, whereas cerebral palsy, traumatic brain injury (TBI), and ALS remained underrepresented.The most prolific countries were the United States, China, Italy, Canada, and the UK.Institutional and co-authorship networks were primarily centered in North America and Western Europe.Keyword mapping revealed prominent themes, including motor recovery, neuroplasticity, and functional improvement.Discussion: This is the first bibliometric study to offer a comprehensive overview of TMS in neurological rehabilitation across multiple disease groups.The findings reveal both significant growth in scientific activity and persistent underrepresentation of certain disorders like ALS and cerebral palsy.Future research should prioritize these gaps through multicenter, long-term studies to broaden the clinical application of TMS in neurorehabilitation
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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 | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".