Worldwide productivity in the field of foot and ankle research from 2009–2013: a bibliometric analysis of highly cited journals
Bibliographic record
Abstract
BACKGROUND: Significant growth has been observed in the field of foot and ankle research in recent years. However, bibliometric studies concerning the quantity and quality of articles published in foot and ankle journals are scarce. This study aimed to reveal the characteristics of national productivity in the field of foot and ankle research and to provide a general picture of foot and ankle research for surgeons and researchers. METHODS: Web of Science was searched for foot and ankle articles in 4 highly cited journals from 2009 to 2013. The number of total articles and citations were collected to evaluate the contribution of different countries. Publication activity was adjusted for the countries by population size and gross domestic product (GDP). RESULTS: A total number of 2083 articles were published worldwide. North America, West Europe, Australia and East Asia were the most productive world regions. High income countries published 90.35% of articles, middle-income 9.60%, and low-income just 0.05%. The United States published the largest number of articles (1025/2083, 49.2%), followed by the United Kingdom (221/2083, 10.6%), Australia (92/2083, 4.4%), and had the highest total citations (3631). However, Canada had the highest average citations per article (5.0), followed by Australia (4.6) and Switzerland (4.2). There were positive correlations between the total number of publications and population/GDP (p < 0.01). When normalized to population size, Switzerland ranked the highest, followed by Australia, and the United Kingdom. When adjusted for GDP, Switzerland ranked the highest, followed by United Kingdom, and South Korea. CONCLUSIONS: The United States is the most productive country in the field of foot and ankle research. However, Australia, some smaller European and Asian countries may be more productive relative to their size.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.082 | 0.122 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".