Trends of Repetitive Transcranial Magnetic Stimulation From 2009 to 2018: A Bibliometric Analysis
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) technology, which is amongst the most used noninvasive brain stimulation techniques currently available, has developed rapidly from 2009 to 2018. However, reports on the trends of rTMS using bibliometric analysis are rare. The goal of the present bibliometric analysis is to analyse and visualise the trends of rTMS, including general (publication patterns) and emerging trends (research frontiers), over the last 10 years by using the visual analytic tool CiteSpace V. Publications related to rTMS from 2009 to 2018 were retrieved from the Web of Science (WoS) database, including 2986 peer-reviewed articles/reviews. Active authors, journals, institutions and countries were identified by WoS and visualised by CiteSpace V which could also detect burst changes to identify emerging trends. GraphPad Prism 8 was used to analyse the time trend of annual publication outputs. The USA ranked first in this field. Pascual-Leone A (author A), Fitzgerald PB (author B), George MS (author C), Lefaucheur JP (author D), and Fregni F (author E) made great contributions to this field of study. The most prolific institution to publish rTMS-related publications in the last decade was the University of Toronto. The journal Brain Stimulation published most papers. Lefaucheur et al.’s paper in 2014, and the keyword ‘noninvasive brain stimulation’ showed the strongest citation bursts by the end of 2018, which indicates increased attention to the underlying work, thereby indicating the research frontiers. This study reveals the publication patterns and emerging trends of rTMS based on the records published from 2009 to 2018. The insights obtained have reference values for the future research and application of rTMS.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.085 | 0.123 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".