Worldwide productivity in the hand and wrist literature: A bibliometric analysis of four highly cited subspecialty journals
Bibliographic record
Abstract
OBJECTIVE: Hand and wrist research has recently shown obvious progress. The quantity and quality of publications from different nations, however, have not been analyzed. In our study, we aimed to assess the characteristics of worldwide productivity in hand and wrist literature using highly cited subspecialty journals. METHODS: Literature search using the Web of Science database was conducted to identify hand and wrist articles in four highly cited subspecialty journals from 2005 to 2014. The number of articles, impact factors and citations were analyzed to evaluate the contributions of different countries. Publication activity was adjusted for the countries by population size. RESULTS: A total of 4268 publications were identified. The number of articles showed a significant increase of 2.10-fold between 2005 and 2014 (p = 0.0001). North America, West Europe, and East Asia were the most prolific areas. The majority of publications (92.03%) were from high-income countries, 7.97% from middle-income countries, and no publications from lower-income countries. The United States published the most articles (53.89%), followed by United Kingdom (6.51%), Japan (6.14%), Canada (3.70%), and China (3.37%). Articles originating from the United States showed the greatest number of total 5-year impact factors (5y-IF) (4059.56) and total citations (17,998). When normalized to population size, United States ranked the first (7.16), followed by Sweden (6.53), and Netherlands (5.72). However, Netherlands (1.893) had the highest mean 5y-IF, followed by Germany (1.884) and Australia (1.883). Sweden had the highest average citations per article (11.38), followed by Germany (9.63), and Australia (9.08). CONCLUSIONS: The number of publications of hand and wrist research shows a significant increase during the past 10 years. The United States is the most productive country in hand and wrist literature. However, some European countries and Australia may have higher quality of articles according to mean 5y-IF and mean citations per article.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.127 | 0.118 |
| 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) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".