Systematic review of the tools and outcomes for the assessment of acute radiation dermatitis severity
Bibliographic record
Abstract
Acute radiation dermatitis (ARD) is a common adverse effect experienced by patients undergoing radiation therapy. Effective assessment tools to accurately measure and manage ARD's impact on patients' quality of life and treatment outcomes is needed. The aim of this study was to evaluate the diverse tools and outcome measures used in the assessment of ARD. A systematic review of MEDLINE®, Embase, and Cochrane was conducted for records 1946-March 2024. A total of 423 studies, including 227 randomized controlled trials (RCTs), were included in the analysis. The review identified 58 distinct tools utilized in the assessment of ARD including clinician and patient-reported outcomes, quality-of-life instruments, and biophysical measures. The most frequently employed tools were the Radiation Therapy Oncology Group criteria, Common Terminology Criteria for Adverse Events, Skindex-16, Visual Analogue Scale, Dermatology Life Quality Index, European Organization for Research and Treatment of Cancer Quality of Life C30, Radiation-Induced Skin Reaction Assessment Scale, Skin Toxicity Assessment Tool, photography, reflectance spectrophotometry, and the corneometer. This investigation into ARD assessment tools reveals a broad application of instruments but is limited by a pervasive lack of consensus, preventing the endorsement of a standardized toolset. The variability in tool use necessitates further research, particularly high-quality RCTs, to establish validated and reliable measures. Future publications, including Delphi consensus-based recommendations, are anticipated to address these gaps, aiming to standardize ARD assessment methodologies.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".