Bibliometric analysis of the uveitis literature and research trends over the past two decades
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the publication patterns and present a current view of the field of uveitis using a bibliometric analysis. DESIGN: Bibliometric analysis. METHODS AND ANALYSIS: A comprehensive search of three databases including MEDLINE, EMBASE and Cochrane was conducted from 1 January 2000 to 31 December 2022. Search results from all three databases were subjected to analysis by Bibliometrix, an R programme that analyses large literature dataset with statistical and mathematical models. Visualisation of collaboration networks and relevance between countries was presented with VOSviewer. RESULTS: A total of 26 296 articles were included in the analysis. The field of uveitis has undergone a significant exponential growth since 2000, with an average growth rate of 4.14%. The most substantial annual growth was between the years 2021 and 2022 (36%). According to the corresponding author's countries, the three most productive countries were Turkey (3288, 12.6%), the USA (3136, 12%) and Japan (1981, 7.6%). The USA (243, 31.4%), England (117, 15%) and Germany (62, 8%) are the top three countries that contributed to clinical trials. The average international collaboration of all countries was 2.5%. CONCLUSIONS: Uveitis literature has undergone significant growth in the past two decades. The demographic factors of publishing countries lead to their various productivity and types of these uveitis studies, which is closely associated with the countries' scientific research resources and patient populations.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.140 | 0.184 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".