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Record W4205102453 · doi:10.5935/0004-2749.20220081

Trends in Graves’ orbitopathy research in the past two decades: a bibliometric analysis

2021· article· en· W4205102453 on OpenAlexaboutno aff
Khaled Ali Elubous, Ali D. Alebous, Hebah A. Abous, Rawan A. Elubous, Lana A. Alebous, Taher Alshammari

Bibliographic record

VenueArquivos Brasileiros de Oftalmologia · 2021
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCluster (spacecraft)MedicineGraves' diseaseGeographyLibrary scienceDemographyDiseasePathologyComputer scienceSociology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0940.113
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.114
GPT teacher head0.440
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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