Predictive Factors of Overall Well-Being Using the EORTC QLQ-C15-PAL Extracted from the EORTC QLQ-C30
Bibliographic record
Abstract
OBJECTIVE: The European Organization of Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire - Core 15 Palliative (EORTC QLQ-C15-PAL) was developed to assess quality of life (QOL) for the palliative cancer population to decrease patient burden. The purpose of this study was to compare predictive factors for well-being in the QLQ-C15-PAL extracted from the EORTC Quality of Life Questionnaire - Core 30 (QLQ-C30) with the QLQ-C30 itself. METHODS AND MATERIALS: Patients with advanced cancer referred for treatment of bone metastases completed the QLQ-C30. Fifteen items from the QLQ-C15-PAL were extracted from the QLQ-C30. Univariate and multivariate analyses were used to determine predictive factors of the global QOL/health score in both tools. In the multivariate analyses, a p value of <0.003 indicated statistical significance. RESULTS: Overall, predictive factors were similar when analyzing data from both tools. Predictive factors for the QLQ-C30 were role functioning (p<0.0001), fatigue (p<0.0001), nausea/vomiting (p<0.0001), and financial problems (p<0.0001) and factors for the extracted QLQ-C15-PAL were physical functioning (p<0.0001) and fatigue (p<0.0001). CONCLUSIONS: Extraction of the QLQ-C15-PAL items from the QLQ-C30 resulted in similar predictive QOL domains for all patient subgroups analyzed individually. The QLQ-C15-PAL is reflective of the QLQ-C30 domains and is recommended for future studies involving patients in a palliative setting, as this shorter questionnaire reduces patient burden and may increase accrual and compliance, while maintaining a similar breadth of coverage and achieving the same predictive ability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".