Bibliometric and Visual Analysis of Elbow Tendinosis Research in Orthopaedic Surgery
Bibliographic record
Abstract
Aim: Elbow tendinosis is the most frequent cause of elbow discomfort. The disease is common in the daily routine practice of sports medicine and orthopedics. However, currently, this issue lacks a multifaceted, methodical, and understandable visual examination. Materials and Methods: Utilizing the Biblioshiny program, the core collection dataset of the Web of Science database's literature on elbow tendinosis from 1970 to April 2023 was compiled and examined. Research hotspots and development patterns were examined in terms of highly influential authors, research institutions, nations or regions, keywords, and referenced publications. Results: According to the search criteria, 526 articles were published by 1817 authors from 839 affiliations, and 47 countries in the Web of Science database. The amount of articles on elbow tendinosis has increased over time, especially after 2000. 55.6% of all articles were published in 2010 and later years. The articles included in this study were published in the United States (n=152, 28.843%), England (n=43, 8.159%), Germany (n=40, 7.59%), Turkey (n= 40, 7.59%) and South Korea (n=28, 5.313%). The United States had the highest total citation number 4493, but Canadian publications had the highest number of average article citations (56.4). Conclusion: Although studies on elbow tendinosis in the field of orthopedics have gained momentum in recent years, they are still insufficient. Although the United States ranks first in terms of publications, it is pleasing for our country that Turkey ranks high.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.195 | 0.169 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".