Prevalence of oral side effects associated with chemo and radiotherapy in head and neck cancer treatment: A cross-sectional study in Egypt
Bibliographic record
Abstract
different treatment modalities used for treating head and neck cancer are associated with oral side effects that affect patients’ quality of life. This study aims to detect the prevalence of different oral side effects associated with chemo and radiotherapy in head and neck cancer. A cross-sectional study was conducted on 294 head and neck cancer patients, clinical examination of the oral cavity was done in which oral mucositis, xerostomia, altered taste sensation, pain, oral fungal infections, and dysphagia were documented and graded as well as Edmonton Assessment Symptom Scale and University of Washington questionnaire v.4 (UWQOL). The prevalence of oral side effects among 294 HNC patients was 93.9%. The most common one was oral mucositis 97.3% followed by xerostomia 84.4%, while 64.6% had dysphagia,53.7% complained of altered taste sensation, and 19% had oral fungal infections and pain mean score of 4. Postoperative radiotherapy is associated with Grade (2) oral mucositis and dysphagia, radical radiotherapy is associated with Grade (1) oral mucositis and pain while severe dysphagia is associated with combined therapy. According to quality of life questionnaire, the best score reported by UWQOL v.4 was shoulder pain while the worst score was for anxiety and mood. There was worsening of physical and social domains of quality of life with chemoradiotherapy and overall patient condition. Head and neck cancer patients suffer from severe oral side effects influenced by the treatment modality and stage of the tumor affecting their quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".