Efficacy of Melatonin Oral Gel in Reducing Oral Mucositis in Patients with Head and Neck Cancer under Chemoradiation
Bibliographic record
Abstract
Objective: Oral Mucositis is a significant burden for patients receiving radiation treatment for head and neck cancer. This study aimed to evaluate the efficacy of melatonin oral gel in reducing radiation induced oral mucositis. Patients and methods: This is prospective, randomized clinical controlled study including locally advanced head and neck cancer patients receiving chemoradiation. The selected participants were recruited into two groups (40 individuals each). Group 1 (melatonin group) will receive 20 mg/ 10 ml /twice per day melatonin gel mouth wash, along with the conventional treatment. Group 2 (control group) will receive conventional treatment only. All participants were evaluated by oral mucositis scale and visual analog scale for pain. Reduced glutathione was quantitated using GSH Colorimetric Assay Kit at base line and on the last day of treatment. Results: All patients in both treatment groups had low-grade mucositis (grade 1–2). After two weeks, 30% of the patients in the control group had developed severe mucositis (grade 3–4), meanwhile this grade was not reported in melatonin group. At the end of study, 80% of the cases had severe mucositis in the control group compared to the melatonin group (10%) with p value 0.001. At the end of each week, the mean values of pain score were significantly lower in melatonin arm compared to control arm. Also, the mean level of salivary GSH significantly increased in both groups(39.5 ± 2.7 and 29.2 ± 3.85) for the melatonin and control groups, respectively as compared to the baseline value and there were statistically significant differences in Favor to melatonin group (group I; P= 0.001). Conclusion: Oral mucositis is a clinically critical consequence of chemoradiation. Melatonin significantly reduces pain and irradiation induced mucositits through oncostatic and cytoprotective mechanisms.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".