A Pilot Study Using the Chinese Herbal Paste Liu-He-Dan to Manage Radiodermatitis Associated with Breast Cancer Radiotherapy
Bibliographic record
Abstract
BACKGROUND: During radiotherapy for breast cancer, patients are greatly affected by pain, infection, and delayed healing of wounds caused by radiodermatitis. In the present study, we aimed to determine the efficacy of Liu-He-Dan in treating radiodermatitis. METHODS: In 26 breast cancer patients who experienced moist decrustation while receiving radiotherapy, 5 g Liu-He-Dan was applied externally once daily after the wound surface had been cleaned and dried. The healing time was recorded, and a Kaplan-Meier survival curve was applied to analyze the treatment course. Meanwhile, a pain assessment using the Numeric Rating Scale (nrs) recorded the pain level experienced by patients after application of the Liu-He-Dan. RESULTS: After application of Liu-He-Dan, the average healing time for the surface of the moist decrustation wounds was 14.17 ± 2.03 days (range: 5-22 days). Inflammatory seepage decreased significantly and exudation almost disappeared in 3 days. The pain trend line indicated that the average nrs score declined with treatment in all patients. The average nrs scores at days 1, 4, and 7 were 6.13, 3.62, and 2.58 respectively. After 3 days of treatment, pain was remarkably alleviated in 80.76% of patients. After treatment for 1 week, the pain remission rate was 96.15%, without any obvious adverse reactions. CONCLUSIONS: Liu-He-Dan was efficacious in treating radiation skin injury with little toxicity and few side effects; the economic efficiency of the treatment was also favourable. The Liu-He-Dan was generally well tolerated by patients. In future, randomized control trials will be established for further observation of the value of Liu-He-Dan in treating radiodermatitis in breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".