Radiodermatitis in Patients With Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PROBLEM IDENTIFICATION: A systematic review and meta-analysis was conducted to inform the development of guidelines on the management of radiodermatitis among patients with cancer. LITERATURE SEARCH: The authors updated a systematic review to include available literature published through September 30, 2019. DATA EVALUATION: Two investigators assessed risk of bias using the Cochrane Collaboration risk-of-bias tool and certainty of the evidence using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. SYNTHESIS: The use of deodorant/antiperspirant had no effect on development of radiodermatitis. Aloe vera and emu oil were equivalent or less effective than standard care. Oral curcumin had a minimal beneficial effect. Nonsteroidal topical interventions had a minimal beneficial effect on the development of moist desquamation and relief of itching while causing a small increase for grade 2 radiodermatitis. Topical calendula increased risk for the development of radiodermatitis. Topical steroids and dressings each showed benefits to minimize the development of radiodermatitis and moist desquamation while lowering rates of patient-reported symptoms, such as pain and pruritus. IMPLICATIONS FOR RESEARCH: Symptom management strategies for radiodermatitis among patients with cancer that are likely to be effective include topical nonsteroidals, topical steroids, and dressings. SUPPLEMENTAL MATERIAL CAN BE FOUND AT HTTPS: //bit.ly/2FWj3Kp.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".