ONS Guidelines™ for Cancer Treatment–Related Skin Toxicity
Bibliographic record
Abstract
BACKGROUND: Management of cancer treatment-related skin toxicities can minimize treatment disruptions and improve patient well-being. OBJECTIVES: This guideline aims to support patients and clinicians in decisions regarding management of cancer treatment-related skin toxicities. METHODS: A panel developed a guideline for management of cancer treatment-related skin toxicities using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) for certainty of evidence and the National Academies of Sciences, Engineering, and Medicine criteria for trustworthy guidelines. The Cochrane risk-of-bias tool assessed risk of bias. A quantitative or narrative synthesis of the evidence was completed. RESULTS: The panel issued seven conditional recommendations for epidermal growth factor receptor inhibitor rash, hand-foot skin reaction, hand-foot syndrome, and chemotherapy-induced alopecia. The panel suggested strategies for prevention and treatment for all toxicities except hand-foot syndrome, which only has a prevention recommendation. IMPLICATIONS FOR NURSING: Cancer treatment-related skin toxicities can significantly affect quality of life. Incorporation of these interventions into clinical care can improve patient outcomes. SUPPLEMENTARY MATERIAL CAN BE FOUND AT HTTPS: //onf.ons.org/supplementary-material-ons-guidelines-cancer-treatment-related-skin-toxicity.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".