ONS Guidelines™ for Cancer Treatment–Related Radiodermatitis
Bibliographic record
Abstract
PURPOSE: Radiodermatitis is a side effect of radiation therapy. Evidence-based interventions to minimize severity or delay progression are important for clinical care. This guideline intends to support individuals with cancer, clinicians, and others in decisions regarding radiodermatitis treatment. METHODOLOGIC APPROACH: A panel of healthcare professionals with patient representation was convened to develop a national clinical practice guideline for the management of radiodermatitis. GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology and the National Academies of Sciences, Engineering, and Medicine criteria for trustworthy guidelines were followed. The Cochrane Collaboration risk-of-bias tool was used, and certainty of the evidence was assessed using the GRADE approach. A quantitative and narrative synthesis of the evidence was completed. FINDINGS: The panel agreed on eight recommendations and made a conditional recommendation for deodorant/antiperspirant. Aloe vera and oral curcumin had knowledge gaps and were recommended only in the context of a clinical trial. The panel suggested against emu oil, calendula, and nonsteroidal interventions. IMPLICATIONS FOR NURSING: This guideline summarizes evidence-based interventions for the management of radiodermatitis to guide clinical care. SUPPLEMENTARY MATERIAL CAN BE FOUND AT HTTPS: //bit.ly/2GEwJtT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 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".