Interventions for the Prevention of Oral Mucositis in Patients Receiving Cancer Treatment: Evidence from Randomised Controlled Trials
Bibliographic record
Abstract
Oral mucositis is a common and most debilitating complication associated with cancer therapy. Despite the significant clinical and economic impact of this condition, there is little to offer to patients with oral mucositis, and the medications used in its management are generally only palliative. Given that mucositis is ultimately a predictable and, therefore, potentially preventable condition, in this study we appraised the scientific literature to evaluate effective methods of prevention that have been tested in randomised controlled trials (RCTs). Published high-level evidence shows that multiple preventative methods are potentially effective in the prevention of oral mucositis induced by radiotherapy, chemotherapy, or both. Anti-inflammatory medications (including benzydamine), growth factors and cytokines (including palifermin), cryotherapy, laser-and-light therapy, herbal medicines and supplements, and mucoprotective agents (including oral pilocarpine) showed some degree of efficacy in preventing/reducing the severity of mucositis with most anticancer treatments. Allopurinol was potentially effective in the prevention of radiotherapy-induced oral mucositis; antimicrobial mouthwash and erythropoietin mouthwash were associated with a lower risk of development of severe oral mucositis induced by chemotherapy. The results of our review may assist in highlighting the efficacy and testing the effectiveness of low-cost, safe preventative measures for oral mucositis in cancer patients.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".