Global trends in research on MOG antibody-associated disease: bibliometrics and visualization analysis
Bibliographic record
Abstract
Objective The purpose of this study was to investigate the current research status, focus areas, and developmental trends in the field of Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) through an analysis of scientific literature. Methods The relevant research articles on MOGAD published from 1947 to 2022 were retrieved from the Web of Science database. The quantitative output of MOGAD related research articles, their distribution by country/region, data on collaborative publishing, influential authors, high-yield institutions, keywords, hotspots, and development trends were analyzed. Additionally, visual knowledge maps were generated using VOSviewer and Citespace. Results There has been a steady increase in the number of MOGAD related publications indicating that the subject has garnered increasing interest among researchers globally. The United States has been the leading contributor with 496 papers (19.25%), followed by China (244, 9.63%), Japan (183, 7.10%), the United Kingdom (154, 5.98%), and Germany (149, 5.78%). Among these countries, the United Kingdom boasts the highest citation frequency at the rate of 46.49 times per paper. Furthermore, active collaboration in MOGAD related research is observed primarily between the United States and countries such as Canada, Germany, Australia, Italy, the United Kingdom and Japan. Mayo Clinic ranks first in total articles published (109) and frequency of citations per article (77.79). Takahashi Toshiyuki from Tohoku University is the most prolific author, while Multiple Sclerosis and Related Disorders is the most widely read journal in this field. “Disease Phenotype”, “Treatment”, “Novel Coronavirus Infection and Vaccination”, “Immunopathological Mechanisms”, “Clinical characteristics of children” and “Prognosis” are the primary keywords clusters in this field. “Novel Coronavirus Infection and Vaccination” and “Immunopathological Mechanisms” are research hotspots and have great development potential. Conclusion The past three decades have witnessed a significant expansion of research on MOGAD. The pathogenetic mechanism of MOGAD is poised to be the prominent research focus in this field in the foreseeable future.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.134 | 0.156 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".