Overview of Graves Ophthalmopathy Literature From 1999 to 2019: Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Research on Graves ophthalmopathy has increased remarkably over the last 2 decades; however, few statistical analyses of the data presented in these publications have been conducted. OBJECTIVE: This study aims to detect and analyze emerging trends and collaboration networks in Graves ophthalmopathy research. METHODS: Graves ophthalmopathy-related publications from 1999 to 2019 were collected from the Web of Science Core Collection Database. Collected publications were restricted by category (article or review) and language (English). Bibliometric analyses included changes in the annual numbers of publications, journals, authors, countries, institutions, keywords, and references. RESULTS: In total, 3051 publications that met the criteria were collected. The number of annual publications has exhibited an increasing trend over the last 20 years. The journal Thyroid ranked first, publishing 183 Graves ophthalmopathy-related studies. There was no evidence of a relationship between impact factor (IF) and the number of publications (P=.69). The author Smith TJ had the largest number of publications on Graves ophthalmopathy (n=83). Of the countries that had published Graves ophthalmopathy-related articles, the United States had the largest number (n=784) and the highest centrality (0.18). Among institutions, the University of Pisa (Italy) contributed the most Graves ophthalmopathy-related articles (n=114). The most recent burst keywords (proliferation, rituximab, and selenium) and references may provide clues on emerging trends in research and clinical practice. CONCLUSIONS: This bibliometric analysis highlights countries, institutions, and authors who contributed to Graves ophthalmopathy-related publications. Emerging trends in Graves ophthalmopathy research, based on burst keywords and references, may provide clues relevant to clinical practice and future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.095 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".