Does change in health-related quality of life score predict survival? Analysis of EORTC 08975 lung cancer trial
Bibliographic record
Abstract
BACKGROUND: Little is known about whether changes in health-related quality of life (HRQoL) scores from baseline during treatment also predict survival, which we aim to investigate in this study. METHODS: We analysed data from 391 advanced non-small-cell lung cancer (NSCLC) patients enrolled in the EORTC 08975 study, which compared palliative chemotherapy regimens. HRQoL was assessed at baseline and after each chemotherapy cycle using the EORTC QLQ-C30 and QLQ-LC13. The prognostic significance of HRQoL scores at baseline and their changes over time was assessed with Cox regression, after adjusting for clinical and socio-demographic variables. RESULTS: After controlling for covariates, every 10-point increase in baseline pain and dysphagia was associated with 11% and 12% increased risk of death with hazard ratios (HRs) of 1.11 and 1.12, respectively. Every 10-point improvement of physical function at baseline (HR=0.93) was associated with 7% lower risk of death. Every 10-point increase in pain (HR=1.08) was associated with 8% increased risk of death at cycle 1. Every 10-point increase in social function (HR=0.91) at cycle 2 was associated with 9% lower risk of death. CONCLUSIONS: Our findings suggest that changes in HRQoL scores from baseline during treatment, as measured on subscales of the EORTC QLQ-C30 and QLQ-LC13, are significant prognostic factors for survival.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".