Effect of honey-ginger mouthwash on oral mucositis in patients undergoing chemotherapy
Bibliographic record
Abstract
Background Oral mucositis is considered as one of the most prevalent complications of chemotherapy or radiation therapy in cancerous tumors, which can interrupt the patient’s treatment and nutrition. This study therefore aimed to evaluate the efficacy of ginger-honey mouthwash on the prevention of chemotherapy-induced oral mucositis in patients suffering from various cancers.Materials and methods In this randomized clinical trial study, 70 patients receiving chemotherapy were divided into case and control groups. The former group (n = 34) received natural honey-ginger mouthwash and the latter (n = 36) used normal saline for 14 days. The presence and severity of oral mucositis, pain intensity, and other related characteristics were evaluated based on a two-part questionnaire (demographic and clinical information) and a checklist prepared from the protocols of the World Health Organization in each group.Results During a 14-day intervention, patients received a 7-day intervention with ginger-honey mouthwash revealed a significant reduction in the mean severity of oral mucositis compared to the control group (p = 0.03). However, a 14-day intervention with ginger-honey mouthwash indicated no significant impact on the mean severity of oral mucositis (p = 0.6). In addition, no significant difference was observed in pain intensity between case and control groups during these 14 days.Conclusions This study suggests that a seven-day intervention with ginger-honey mouthwash has a beneficial effect on reducing the severity of mucositis in patients under chemotherapy, unlike a 14-day intervention. The honey-ginger mouthwash fails to have a significant effect on the pain intensity due to mucositis in patients undergoing chemotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".