297P Modified Delphi consensus on interventions for radiation dermatitis in breast cancer: A Canadian expert perspective
Bibliographic record
Abstract
Acute radiation dermatitis is a prevalent adverse effect of radiotherapy in patients with breast cancer, and there is a lack of high-quality data regarding its prevention and management, which leads to a lack of standardized care This study employs a modified Delphi process to compile the perspectives of Canadian breast cancer radiation oncology and dermatology experts, aiming to establish consensus-based recommendations for the prevention and management of acute radiation dermatitis in breast cancer patients. A four-round modified Delphi consensus process was organised with the participation of 19 Canadian experts. The process involved a systematic review of existing literature on the prevention and treatment of acute radiation dermatitis in breast cancer, from January 1946 to July 2023. Participants then provided their opinions on the strength and quality of the evidence for the 59 identified interventions. A second round involved assessing the degree to which the intervention would be recommended in either low- or high-risk settings. Two other rounds were used to consolidate consensus, which was determined by achieving a minimum agreement threshold of 75%. With two rounds completed, consensus for recommendation was reached for 2 prevention interventions in both low- and high-risk settings and near-consensus for recommendation was reached for 1 prevention intervention in the high-risk setting. With regards to the management of acute radiation dermatitis, there was consensus for recommendation for 1 product. However, a significant number of interventions did not receive recommendations for either prevention or management due to insufficient or conflicting evidence. Two subsequent rounds are currently being held, and final recommendations are planned for the spring 2024. This pan-Canadian modified Delphi consensus initiative provides expert-reviewed and evidence-based recommendations for interventions to prevent and manage acute radiation dermatitis in breast cancer patients, highlighting areas where consensus among experts has been achieved, with the goal of standardising care and minimizing unnecessary costs.
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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 | 0.244 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".