Mapping current trends and hotspots in myasthenia gravis from 2003 to 2022: a bibliometric analysis
Bibliographic record
Abstract
Introduction Research on myasthenia gravis (MG) has undergone rapid development in recent years. This article aimed to elucidate the characteristics of MG publications over the past 20 years and analyze emerging trends using bibliometric methods. Methods Information on MG articles was obtained from the Web of Science Core Collection and stored in Excel for quantitative analyses. Bibliometric analyses were performed using CiteSpace and VOSviewer to visualize publications according to countries/regions, institutions, journals, and authors. Results A total of 3,610 publications were included in the analysis. The USA had the highest number of publications (NP) and H-index. Among the institutions, the University of Oxford had the highest NP, followed by the University of Toronto and Duke University. Close cooperation was observed among countries and institutions. The most productive author was Renato Mantegazza, followed by Jan J. Verschuuren, and Amelia Evoli. Muscle & Nerve published the most articles on MG, followed by the Journal of Neuroimmunology and Neuromuscular Disorders. The keyword with the highest strength is “neuromuscular transmission,” followed by “safety” and “rituximab.” Co-citation analysis includes 103 publications cited at least 65 times, categorized into four clusters. Additionally, 123 keywords cited more than 40 times were analyzed and divided into five clusters. Conclusion This bibliometric analysis shows the framework of research over the past 20 years by mapping the scholarly contributions of various countries or regions, institutions, journals, and authors in MG. The analysis also explores future trends and prospective directions, emphasizing individualized treatment based on subtypes, novel immunotherapeutic approaches, and thymectomy.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.080 | 0.124 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".