Topical Ectoin versus Topical Dexpanthenol for Managing Acute Radiodermatitis Associated with Breast Cancer Radiotherapy: A Randomized Double-Blind Study
Bibliographic record
Abstract
Abstract: Background: Radiodermatitis is a common side effect of breast cancer radiotherapy; however, there is no current consensus regarding an effective standard therapy. Objective: To evaluate the efficacy of topical ectoin versus dexpanthenol in the management of acute radiodermatitis after breast cancer radiotherapy. Methods: Fifty patients randomly used dexpanthenol 5% cream (25 patients), or ectoin 7% cream (25 patients), applied twice daily to the irradiated area during and for 2 weeks after radiotherapy. The study was stratified by the radiotherapy schedule and was double-blind. Radiodermatitis grade, radiation-associated symptoms, and adverse events were assessed weekly during radiotherapy and 2 weeks thereafter. Skin-related quality of life (QOL) scores were measured using the Skindex-16 questionnaire. Results: Both agents were effective in preventing severe radiodermatitis (≥G3). Ectoin had a lower radiodermatitis grade level than dexpanthenol, with a significant difference at week 2 ( P = 0.008). Radiation-associated pain ( P = 0.003) and itching ( P = 0.001) were lower with ectoin than dexpanthenol. Side effects were not significantly different between the 2 treatments ( P = 0.107). Ectoin showed less QOL impairment than dexpanthenol. The radiation schedule was an independent predictor for radiodermatitis persistence. Conclusion: Ectoin showed some clinical benefit over dexpanthenol in improving radiation dermatitis and the radiation schedule is a predictor of radiodermatitis persistence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".