Mapping Global Trends and Collaborations in Renal Trauma: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Renal trauma is the most common form of genitourinary injury and a major concern in emergency medicine, urology, and traumatology. Despite its clinical significance, comprehensive bibliometric evaluations remain scarce. This study aimed to systematically analyze renal trauma literature published between 2005 and 2024, identify influential contributors, visualize collaboration patterns, and highlight research gaps. Methodology: A bibliometric analysis was conducted using the Web of Science Core Collection. Original research articles in English published between 2005 and 2024 were retrieved with predefined keywords. Bibliometric indicators, including publication counts, citation frequencies, h-index, and citation sum within the h-core (CSh), were calculated. Collaboration networks, keyword co-occurrence, and bibliographic coupling were analyzed using VOSviewer, while BibExcel and Microsoft Excel were used for data processing. Results: A total of 464 original articles authored by 2462 researchers from 46 countries and published in 173 journals were analyzed. The United States of America dominated in publication output and citation impact, followed by France, Canada, and China. The Journal of Urology, Journal of Trauma and Acute Care Surgery, Journal of Trauma–Injury, Infection and Critical Care and Urology were the leading publication venues. Co-occurrence analysis revealed that renal trauma research mainly clusters around the terms kidney, renal trauma, trauma, and wounds and injuries. Conclusion: This study comprehensively maps renal trauma research, underscoring the need for stronger international collaborations, broader representation of low- and middle-income countries, and innovative approaches to optimize clinical management.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.157 | 0.205 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".