Trends in Graves’ orbitopathy research in the past two decades: a bibliometric analysis
Bibliographic record
Abstract
ABSTRACT Purpose: This study was conducted to identify trends in Graves’ orbitopathy research in the past two decades and to elaborate on hot topics in the field. Methods: The Web of Science database was used to extract articles on Graves’ orbitopathy or its synonyms. Full data and references were exported to VOSviewer software to be analyzed. Visualization maps and charts were constructed accordingly. Results: We retrieved 1067 articles on Graves’ orbitopathy from the Web of Science database. The United States ranked first in terms of the article count (25), followed by Italy (141) and the People’s Republic of China (120). Wiersinga’s and the University of Amsterdam’s articles received the highest citation count (1509 and 3052, respectively). The University of Pisa and Thyroid published the highest number of articles (65 and 93, respectively). Co-authorship analysis showed four clusters of country collaborations: red cluster, European countries; green cluster, the United States, Brazil, Canada, South Korea, and Taiwan; a yellow cluster, People’s Republic of China; and blue cluster, Japan, Australia, and Poland. Keyword analysis revealed five clusters of topics: pathogenesis, management, association, quality of life, and surgery. Analysis of co-cited references also revealed five clusters: pathogenesis, management, risk factors, clinical assessment, and surgical management. Conclusion: Research on Graves’ orbitopathy has grown during the past two decades. Hot research topics are pathogenesis, management, risk factors, quality of life, and complications. Research trends have changed in the past two decades. Increasing interest in exploring Graves’ orbitopathy mechanisms and associations is evident. European countries are cooperating in this field of research. The United States has established more extensive international cooperation than other countries. We believe that more international collaboration involving developing countries is required.
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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: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.109 | 0.131 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".