The role of benzydamine in prevention and treatment of chemoradiotherapy-induced mucositis
Bibliographic record
Abstract
PURPOSE: To discuss the role of benzydamine in the prevention and treatment of radiation-induced oral mucositis (OM) in head and neck (H&N) cancer patients. This document represents an expert opinion paper on indications and key-role aspects in OM pathogenesis, prevention and treatment. ORAL MUCOSITIS: OM represents a common side effect of chemotherapy (CHT) and radiotherapy (RT). It consists in a painful erythema involving the oral cavity mucosa, which may progress to ulceration. Five biologically dynamic phases are considered crucial in mucositis: "initiation, signalling, amplification, ulceration and healing". Oral environment and microbiota are fundamental in mucositis development being involved in susceptibility to infections and in ulceration consequences. Different agents against mucositis have been studied and the use of benzydamine is strongly supported in literature. The Multinational Association of Supportive Care in Cancer and International Society for Oral Oncology (MASCC/ISOO) guidelines recommend its use for the prevention of OM in H&N patients undergoing RT and RT/CHT. BENZYDAMINE: Benzydamine is a local anti-inflammatory drug with analgesic properties. It can decrease TNF-α, IL-1β and prostaglandin synthesis, also inhibiting leukocyte-endothelial interactions, neutrophil degranulation, vasodilation and vascular permeability. Literature agrees on the beneficial effects of benzydamine in preventing and reducing oral mucositis severity in H&N cancer patients undergoing RT/CHT. CONCLUSIONS: Mucositis represents a major concern in H&N cancer patients and a clinical and economical issue. A multimodal and multidisciplinary approach is needed for its management. International guidelines recommend benzydamine for OM prevention and treatment in H&N cancer patients, but further "real world" trials should be designed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".