Global hotspots and research trends of radiation-induced skin injury: a bibliometric analysis from 2004 to 2023
Bibliographic record
Abstract
Background Radiation therapy has become an important treatment for many malignant tumours after surgery and for palliative tumour care. Although modern radiotherapy technology is constantly improving, radiation damage to normal tissues is often difficult to avoid, and radiation-induced skin injury (RSI) is a common complication, manifested as skin erythema, peeling, ulceration, and even bone and deep organ damage, seriously affect the quality of life for patients. Basic research and clinical trials related to RSI have achieved certain results, while no researchers have conducted comprehensive bibliometric studies. Objective A comprehensive bibliometric analysis of publications on RSI published between 2004 and 2023 was conducted to identify current hotspots and future directions in this area of study. Methods RSI-related publications published between January 1, 2004, and December 31, 2023, were retrieved from the Web of Science Core Collection (WoSCC) database for analysis using VOSviewer and CiteSpace analytics. Results A total of 1009 publications on RSI from 2004 to 2023 were included in the WoSCC database. The United States had the highest productivity with 299 papers, accounting for 29.63% of the total production, followed by China with 193 papers (19.13%) and Japan with 111 papers (11.00%). In terms of research institutions and journals, the University of Toronto and Journal of Supportive Care in Cancer published the highest number of papers. Professor Edward Chow published the most articles, while Professor Shuyu Zhang was the most cited. The top ten most-cited papers focused on the pathogenesis, prevention, and management of RSI. Keyword co-occurrence analysis and the top 25 keywords with the strongest citation bursts suggest that current research focuses on the pathogenesis, prevention, and treatment management of RSI. Conclusion This study conducted a systematic bibliometric analysis of RSI publications from 2004 to 2023; identified the trends in RSI publications, major research countries, major research institutions, major research journals, major research authors, and major research keywords; and revealed the future development direction and research hotspots of this field. This study provides a valuable reference for future RSI research.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.109 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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, 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".