Research trends and hotspots of high tibial osteotomy in two decades (from 2001 to 2020): a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to comprehensively analyze the global application trend of high tibial osteotomy (HTO) and identify promising research hotspots of HTO based on bibliometrics and visual analysis. METHODS: Publications (articles and reviews) related to HTO from 2001 to 2020 were retrieved from the Web of Science Core Collection database (WOSCC). The country, institution, year, author, journal, average citations per item, H index, title, abstract, keywords of publication, and the top 10 cited articles were extracted and analyzed in detail. The VOSviewer software was used to analyze theco-occurrence of keywords to predict the hotspots of HTO. RESULTS: A total of 1883 articles were included. In the past 20 years, the number of HTO articles has shown an increasing trend in general. The top 3 countries (the USA, Germany, and South Korea) accounted for 49.547% of all articles published. The USA has the largest number of publications. The University of Western Ontario is the largest contributor. The Knee Surgery Sports Traumatology Arthroscopy is the most influential journal. Professors Saito T and Imhoff AB are the leading scholars who made great achievements in the HTO field. The research direction can be divided into the following 5 clusters: "prognosis and outcome", "HTO combined with cartilage restoration techniques", "animal experimental research", "study on bone union and plate fixation at osteotomy", and "surgical technique research". CONCLUSION: In terms of the trend of previous years, an increasing number of literatures related to HTO will be published in the future. The USA is a world leader in the field of HTO. South Korea presented great potential in this area. HTO combined with cartilage restoration techniques, postoperative prognosis and outcome, and surgical technique research may be the future hotspots in the field of HTO research.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.108 | 0.178 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".