Predictive factors of overall quality of life in advanced cancer patients using EORTC QLQ-C30
Bibliographic record
Abstract
OBJECTIVE: To identify which domains/symptoms from the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) were predictive of overall quality of life (QoL) in advanced cancer patients. METHODS: Four hundred and forty seven patients with brain metastases or bone metastases from seven countries were enrolled with regression analysis to determine the predictive value of the QLQ-C30 functional/symptom scores for patient reported overall QoL (question 30), overall health (question 29) and the global health status domain (questions 29 and 30). RESULTS: Worse role functioning, social functioning, fatigue and financial problems were the most significant predictive factors for worse QoL. In the bone metastases subgroup (n = 400), role functioning, fatigue and financial problems were the most significant predictors. In patients with brain metastases (n = 47), none of the EORTC domains significantly predicted worse QOL. CONCLUSION: Deterioration of certain QLQ-C30 functional/symptom scores significantly contributes to worse QoL, overall health and global health status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".