Health-Related Quality of Life As a Survival Predictor for Patients With Localized Head and Neck Cancer Treated With Radiation Therapy
Bibliographic record
Abstract
PURPOSE: To assess the added prognostic value for overall survival (OS) of baseline health-related quality of life (HRQOL) and of early changes in HRQOL among patients with localized head and neck cancer (HNC) treated with radiation therapy. PATIENTS AND METHODS: All 540 patients with HNC who participated in a randomized trial completed two HRQOL instruments before radiation therapy: the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 (EORTC QLQ-C30) and the Head and Neck Radiotherapy Questionnaire. Six months after the end of radiation therapy, 497 trial participants again completed the two HRQOL instruments. During the follow-up, 179 deaths were observed. Multivariate Cox proportional hazards models were used to test whether HRQOL variables, baseline and change, provided additional prognostic value beyond recognized prognostic factors. RESULTS: The baseline EORTC QLQ-C30 physical functioning (PF) score was an independent predictor of OS. The hazard ratio (HR) associated with a 10-point increment in baseline PF was 0.87 (95% CI, 0.81 to 0.94). In multivariate models, the change in HRQOL was significantly associated with OS for most HRQOL dimensions. Among these, PF change was the strongest predictor. The magnitude of the association between PF change and survival decreased over time. At 1 year, the HR associated with a positive PF change of 10 points was 0.75 (95% CI, 0.68 to 0.83). After PF is taken into account, no other HRQOL variable was associated with survival. CONCLUSION: Our findings indicate that both baseline PF and PF change provide added prognostic value for OS beyond established predictors in patients with HNC. Assessing HRQOL could help better predict survival of cancer patients.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 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".