Assessment of Quality of Life in Head and Neck Cancer Patients before and after Radiation Therapy- A Prospective, Analytical Questionnaire-Based Study from a Tertiary Cancer Centre
Bibliographic record
Abstract
Head-and-neck cancer (HNC) is a major global health issue, with significantly higher incidence rates in India compared to other countries. The diagnosis and treatment of HNC can severely affect a patient’s quality of life (QoL). This study aims to evaluate and compare the QoL of head and neck cancer patients before and after radiation therapy with or without chemotherapy. We conducted a prospective study on 45 newly diagnosed HNC patients who underwent radiation therapy, either alone or in combination with chemotherapy at our department. We assessed QoL using the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 (QLQ-C30) for general assessment and the EORTC QLQ H&N-35 for head and neck-specific evaluation before starting of the treatment and 4 weeks after the completion of the treatment. We used paired t-tests, Mann-Whitney U test and Kruskal-Wallis ANOVA for statistical analysis, with a significance level set at p ≤ 0.05. The most commonly affected site was the oral cavity. All participants initially had a good performance status (PS) of either 1 or 2, with Stage IV being the most prevalent. Analysis of the EORTC QLQ-C30 revealed a decline in global health status, and the EORTC QLQ H&N-35 showed deterioration across all domains from pretreatment. On the functional scale, emotional well-being was notably more impaired, while physical, cognitive, and social functions were less affected. Pain, swallowing difficulties, mucositis, and speech issues were identified as the most severely impacted aspects of QoL. In Conclusion, our study shows a significant decline in QoL following treatment due to the acute and subacute side effects of radiation therapy and chemotherapy. These findings highlight the need for comprehensive supportive care to address the broad impact of HNC treatment on patients quality of life.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 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".