Dietary intake effects on severity of cancer treatment‐induced mucositis: A cross‐sectional study
Bibliographic record
Abstract
Abstract Background and Aims Oral mucositis is one of the most serious complications due to chemotherapy and radiotherapy in head and neck cancer treatment. Oral mucositis causes a wide range of clinical signs and symptoms, such as ulcers, pain, and dysphagia. Additionally, because of speech limitations, patients' self‐esteem will decrease, ultimately causing reduced quality of life. The primary objective of this study was to investigate the role of diet in the onset and progress of mucositis induced by chemotherapy and radiation therapy in patients with cancers. Methods In this study, 121 patients with a mean age of 51.43 ± 13.08 years were selected randomly and referred to the cancer institute, where they underwent their first phase of chemotherapy. In this step, patients were examined and their severity of oral mucositis was graded according to the World Health Organization criteria. They completed a 3‐day allergen food recall and dietary recommendations were met. After completing the forms, four questionnaires were filled out for each patient, the patient's nutrition was analyzed using the N4 software, and the amount of macro‐ and micronutrients was measured. Results Micronutrients such as aspartic acid, glycine, serine, proline, alanine, arginine, glutamic acid, and vitamin B12 and macronutrients such as rose water, sausage, beverages, coffee, and lamb meat were examined, and a significant difference was observed between groups (grade 1 and 2 mucositis) ( p < 0.005). In patients with grade 2 mucositis, a lower level of vitamin B12 was reported ( p < 0.005). There is a negative correlation between amounts of macro‐ and micronutrients and grades of oral mucositis. Conclusion It can be concluded that diet plays a considerable role in the severity of oral mucositis caused by cancer treatment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".