Radiation-induced dermatitis: a review of current understanding
Bibliographic record
Abstract
Background. Prevention and treatment of radiation-induced dermatitis that occurs after radiation therapy (RT) significantly impairs the quality of life of patients, among which the most severe are pain and discomfort caused by radiation dermatitis (RD). Purpose. Assess the condition and modern ideas of the development of technologies of prevention and treatment of radiation-induced dermatitis. Materials and Methods. A literature review based on massive digital publications found in the world resources of Scopus and Web of Science Core Collection for 2019–2023. With restrictions on the filters «Years», «Medicine» and «Articles». Results. Information on the terminology «radiation-induced dermatitis», risk factors for RD, some views on the mechanisms associated with their occurrence, and current ideas about their prevention and treatment were systematized. The leading countries – USA and China, leading scientific institutions –(Institut Curie, France; The University of Texas MD Anderson Cancer Center, USA; German Cancer Research Center and Universitätsklinikum Bonn, Germany, and University of Toronto and Sunnybrook Health Sciences Center, Canada, and their scientific topics were identified. Conclusion: The bibliometric analysis of current ideas about the prevention and treatment of RD allowed us to assess the current state and contribution of leading countries and scientific organizations to the development of innovative technologies for the prevention and treatment of RID. The most cited publications were identified, which indicates their high importance and the availability of a wide range of modern tools aimed at reducing and alleviating the manifestations of RD. In the future, it is desirable to create high-quality systematic reviews that will substantiate standardized, best practices for the prevention and treatment of RD for clinical use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".