US Cutaneous Oncodermatology Management (USCOM): A Practical Algorithm
Bibliographic record
Abstract
BACKGROUND: An increasing number of patients survive or are living with cancer. Anticancer treatments frequently have cutaneous adverse events (cAEs) that may severely impact patients' quality of life and interrupt anticancer treatment. The US Cutaneous Oncodermatology Management (USCOM) project aims to improve cancer patients' and survivors' quality of life by offering tools for preventing and managing cAEs. METHODS: An algorithm was designed to reduce the incidence of cAEs, treat cAEs, and maintain healthy skin using general measures and over-the-counter agents to support all healthcare providers treating oncology patients, including physicians, nurses, pharmacists, and advanced providers. The panel used a modified Delphi approach, developed, discussed, and reached a consensus on statements and an evidence-based algorithm. RESULTS: The USCOM algorithm includes education on cAEs for patients and clinicians supporting prevention, treatment, and maintenance using skincare measures before, during, and after cancer treatment. A skincare regimen including hygiene, moisturization, and sun protection products should be safe and effective in helping to minimize cAEs and improving skin conditions such as erythema, xerosis, pruritus, and photosensitivity. The number and quality of studies evaluating skincare formulations and regimens for cAEs are increasing, but the evidence on the benefits of specific formulations is still scarce. CONCLUSIONS: The algorithm focuses on general measures and skincare to prevent or reduce the severity of cAEs. Increased awareness of cAEs by the multidisciplinary team treating and guiding the cancer patient throughout their care may improve patient outcomes. J Drugs Dermatol. 2021;20:9(Suppl):s3-19.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".