The Top 100 Most-Cited Articles on Thyroid Eye Disease: A Bibliometric Study
Bibliographic record
Abstract
Purpose This review analyzed the top 100 most-cited thyroid eye disease (TED) papers.Methods In November 2022, Scopus was searched for the most highly cited TED works since inception. For each paper, journal of origin and impact factor, corresponding author country and specialty affiliation, citation count, publication year, database-affirmed study funding, and open-access status were extracted.Results A total of 76 primary and 24 secondary articles were published between 1969 and 2021 across 29 journals, with a median (range) of 186.5 (133–930) citations. The most cited journal was Journal of Clinical Endocrinology and Metabolism (25 articles; 5126 citations). The most cited article was ‘Graves’ ophthalmopathy’ (Bahn 2010; New England Journal of Medicine; 930 citations). Articles hailed from 10 countries, with most from the United States (38 articles; 9194 citations). Endocrinology (n = 59) and ophthalmology (n = 26) were the most common corresponding authors. Nineteen first authors contributed multiple articles. Only journal impact factor was significantly associated with citation count (p = .0002; ρ = 0.45).Conclusion A variety of medical disciplines, Western countries, and study personnel contributed to highly cited thyroid eye disease research. Thus, this research area is not exceedingly informed by any singular perspective. Further, it can be interpreted with increased confidence for their generalizability of results to patients globally.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.148 | 0.173 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".