Relationships among health-related quality of life indicators in cancer patients: A pooled study of baseline EORTC QLQ-C30 data from 6,739 patients
Bibliographic record
Abstract
9612 Background: Cancer patients frequently experience multiple and co-occuring problems due to their illness and therapies. Clusters are defined as groups of two or more Health-Related Quality of Life (HRQoL) indicators that occur concurrently and may or may not have a common related cause. The objective of this meta-analysis was to identify how HRQoL indicators cluster among cancer patients. Methods: Retrospective pooling of 29 European Organisation for Research and Treatment of Cancer (EORTC) randomized clinical trials, among 10 cancer sites, yielded baseline EORTC QLQ-C30 HRQoL data for a total of 6739 patients. A cluster analysis was performed to identify clusters among the 15 HRQoL scales, via Ward's method. Cronbach's alpha coefficient (α) was used to measure internal consistency. Dendrograms of the HRQoL indicators were plotted for the overall data and for each cancer site. Results: Three main clusters emerged from the pooled dataset: a physical function-related cluster, consisting of physical and role functioning, fatigue and pain (α = 0.83); a psychological function-related cluster, consisting of emotional and cognitive functioning and insomnia (α = 0.64); and a gastrointestinal cluster, consisting of nausea and vomiting and appetite loss (α = 0.68). The same clusters were found in patients with metastatic and non-metastatic disease. The gastrointestinal cluster was reproduced in all 10 cancer sites. We found that pain was not correlated with the other variables of the physical function cluster for patients with brain, colorectal or pancreatic cancer. For the psychological component cluster, cognitive functioning was not correlated with the other variables of the cluster for breast or pancreatic cancer patients, while insomnia was found not to be correlated with the other variables of the cluster for prostate cancer patients. Conclusions: This study shows that relationships among HRQoL indicators exist and that three major constructs can be found: a physical, a psychological and a gastrointestinal component. Understanding these relationships may aid diagnostic criteria, and assessment, management, and prioritization of symptom care. No significant financial relationships to disclose.
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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 | 0.039 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".