Mepitel film for the prevention of radiation dermatitis: A comprehensive review of its efficacy, side effects, physics measurements, patient- and clinician-reported outcomes
Bibliographic record
Abstract
Objective: This review aimed to summarize the benefits, side effects, physics measurements, and patient- and clinician-reported outcomes of Mepitel film (MF) in preventing radiation dermatitis (RD) for cancer patients. Methods: The online database PubMed was searched from inception to April 15, 2024 with the search terms "Mepitel film" or "Mepitel." Articles of any study design evaluating MF for the prevention of RD were included. Non-human studies were excluded. Results: The database search identified 119 articles and 13 of them were included in this review. Across these studies, MF was found to be beneficial in reducing RD and improved patient- and clinician-reported outcomes in breast and head and neck cancers. Side effects of MF included itchiness, acne, allergic reaction, tightness, discomfort, and poor film adherence, but patient dropouts were uncommon. MF did not cause a bolus effect or increased skin dose in physics measurements. Conclusions: MF is a safe and effective intervention for preventing acute RD. It should be recommended in breast cancer patients where the data is more robust. Further research is needed to evaluate MF's efficacy on patients with different skin tones, its cost-effectiveness, and identifying patients who most benefit from MF relative to other effective interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".