MASCC clinical practice guidelines for the prevention and management of acute radiation dermatitis: part 1) systematic review
Bibliographic record
Abstract
Acute radiation dermatitis (ARD) commonly develops in cancer patients undergoing radiotherapy and is often characterized by erythema, desquamation, and pain. A systematic review was conducted to summarize the current evidence on interventions for the prevention and management of ARD. Databases were searched from 1946 to September 2020 to identify all original studies that evaluated an intervention for the prevention or management of ARD, with an updated search conducted in January 2023. A total of 235 original studies were included in this review, including 149 randomized controlled trials (RCTs). Most interventions could not be recommended due to a low quality of evidence, lack of supporting evidence, or conflicting findings across multiple trials. Photobiomodulation therapy, Mepitel® film, mometasone furoate, betamethasone, olive oil, and oral enzyme mixtures showed promising results across multiple RCTs. Recommendations could not be made solely based on the published evidence due to limited high-quality evidence. As such, Delphi consensus recommendations will be reported in a separate publication.
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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".