Prevention and treatment of acute radiation-induced skin reactions: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Radiation-induced skin reaction (RISR) is a common side effect that affects the majority of cancer patients receiving radiation treatment. RISR is often characterised by swelling, redness, pigmentation, fibrosis, and ulceration, pain, warmth, burning, and itching of the skin. The aim of this systematic review was to assess the effects of interventions which aim to prevent or manage RISR in people with cancer. METHODS: We searched the following databases up to November 2012: Cochrane Skin Group Specialised Register, CENTRAL (2012, Issue 11), MEDLINE (from 1946), EMBASE (from 1974), PsycINFO (from 1806), CINAHL (from 1981) and LILACS (from 1982). Randomized controlled trials evaluating interventions for preventing or managing RISR in cancer patients were included. The primary outcomes were development of RISR, and levels of RISR and symptom severity. Secondary outcomes were time taken to develop erythema or dry desquamation; quality of life; time taken to heal, a number of skin reaction and symptom severity measures; cost, participant satisfaction; ease of use and adverse effects. Where appropriate, we pooled results of randomized controlled trials using mean differences (MD) or odd ratios (OR) with 95% confidence intervals (CI). RESULTS: Forty-seven studies were included in this review. These evaluated six types of interventions (oral systemic medications; skin care practices; steroidal topical therapies; non-steroidal topical therapies; dressings and other). Findings from two meta-analyses demonstrated significant benefits of oral Wobe-Mugos E for preventing RISR (OR 0.13 (95% CI 0.05 to 0.38)) and limiting the maximal level of RISR (MD -0.92 (95% CI -1.36 to -0.48)). Another meta-analysis reported that wearing deodorant does not influence the development of RISR (OR 0.80 (95% CI 0.47 to 1.37)). CONCLUSIONS: Despite the high number of trials in this area, there is limited good, comparative research that provides definitive results suggesting the effectiveness of any single intervention for reducing RISR. More research is required to demonstrate the usefulness of a wide range of products that are being used for reducing RISR. Future efforts for reducing RISR severity should focus on promising interventions, such as Wobe-Mugos E and oral zinc.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".