Worldwide research productivity in fracture surgery: A 10-year survey of publication activity
Bibliographic record
Abstract
Worldwide research contributions have allowed the field of fracture surgery to progress. However, to the best of our knowledge, no studies have documented the main characteristics of publications from different countries. The present study aimed to determine the quantity and quality of worldwide research in fracture surgery. The Web of Science database was searched to identify fracture articles published between 2005 and 2014. The contributions of countries were evaluated based on paper and citation numbers, and the research output of each country was adjusted according to population size. A total of 19,423 papers on the topic of fracture surgery were identified worldwide, and the total number of publications from 2005 to 2014 had significantly increased by 1.82-fold (P<0.001). The majority of papers (86.64%) were published by high-income countries (gross national income per capita ≥$12,736), 13.25% by middle-income countries ($1,046-12,735) and 0.11% by low-income countries (≤$1,045). The United States contributed the highest number of publications (33.34%), followed by the United Kingdom (9.03%), Germany (8.42%), China (5.58%) and Japan (4.18%). Furthermore, the United States ranked first according to total citations (72,640). Articles from Sweden achieved the highest average citations per paper (15.63), followed by Australia (12.84) and Canada (12.44). When the number of publications were adjusted for population size, Switzerland was the first (56.39), followed by Austria (35.43) and the Netherlands (30.68). In conclusion, the number of publications in fracture surgery increased from 2005 to 2014, and the majority of fracture papers were published by high-income countries, while few papers were published by low-income countries. The United States was the most prolific country, but based on population size, a number of smaller countries in Europe may be relatively more prolific.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.031 | 0.049 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".