Interventions for the Prevention of Oral Mucositis in Patients Receiving Cancer Treatment: Evidence from Randomised Controlled Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| 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 it