Predictive factors associated with radiation dermatitis in breast cancer
Bibliographic record
Abstract
PURPOSE: Radiation dermatitis (RD) is a side effect that frequently arises during radiotherapy (RT) in breast cancer patients. The present study investigates possible predictive factors of RD, as well as the use of skin treatments to manage symptoms. METHODS: Demographic and treatment characteristics were collected retrospectively, while skin symptoms and treatments were collected prospectively for patients who received adjuvant RT between December 2013 and November 2015. Patients were seen weekly by clinicians throughout treatment, during which a clinician-reported survey was completed on RD symptoms and skin treatments. Possible predictive factors were correlated with skin outcomes through a univariate ordinal logistic regression analysis. RESULTS: ) (p = 0.0004) and boost (p = 0.02) were predictive of edema. A dose of 50 Gy/25 (p<0.0001) and a high irradiated tissue volume (p = 0.0001) were predictive of desquamation. A dose of 50 Gy/25 (p = 0.0005) and high BMI (p = 0.02) were predictors of pain. Bolus use was the only factor associated with bleeding (p = 0.02). Patients who developed desquamation were likely to receive corticosteroids/antihistamines (p<0.0001), topical antibiotics/antifungals (p<0.001), and dressings (p<0.0001). CONCLUSION: The findings of this study provide evidence of potential predictors of RD and methods of symptom management based on symptom severity. Prevention of RD is needed among high-risk groups, such as patients with a high BMI or receiving a standard fractionation, boost, or bolus.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".