What QLQ-C15-PAL Symptoms Matter Most for Overall Quality of Life in Patients With Advanced Cancer?
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated the QLQ-C15-PAL health-related quality of life (QOL) questionnaire, an abbreviated version of the QLQ-C30 questionnaire that was designed specifically for patients with advanced cancer. The present study assessed whether certain symptoms or functional domains from the QLQ-C15-PAL predicted overall QOL when rated prior to palliative radiation treatment (RT). PATIENTS AND METHODS: Patients attending an outpatient palliative radiotherapy clinic completed QLQ-C15-PAL questionnaires prior to palliative RT for bone, brain or lung disease. Pearson correlations were computed between the QLQ-C15-PAL functional/symptom scores and overall QOL scores. Multiple linear regressions were used to evaluate the relative importance of functional/symptom scales in association with overall QOL. RESULTS: Data from 369 patients were analyzed. The QLQ-C15-PAL domains of physical and emotional functioning, pain, and appetite loss were significant predictors of overall QOL in these patients with advanced cancer. Appetite loss was the only significant independent predictor of overall QOL in the subgroup of patients with advanced lung cancer (n = 29). Both appetite loss and emotional functioning were independently predictive of overall QOL in patients with bone metastases (n = 190). In patients with brain metastases (n = 150), independent predictors of overall QOL included physical and emotional functioning as well as fatigue. CONCLUSION: The QLQ-C15-PAL domains of physical and emotional functioning, pain and appetite loss were significant predictors of overall QOL in this cohort of patients with advanced cancer. Different functional and symptom scales predicted overall QOL in patients with bone metastases, brain metastases or advanced lung cancer.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".