Factors Influencing Health Related Quality of Life in Cancer Patients with Bone Metastases
Bibliographic record
Abstract
OBJECTIVE: Health related quality of life (HRQOL) is a multidimensional concept that is especially important for cancer patients with bone metastases, as maintaining and improving HRQOL is often the main focus of treatment. This study aims to determine factors that may influence HRQOL, which may in turn influence treatment and care of patients. METHODS: Patients (n=396) completed the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ) Bone Metastases module (BM22) at baseline. The EORTC QLQ-BM22 consists of four scales: painful site (PS), pain characteristics (PC), functional interference (FI), and psychosocial aspect (PA) scales. EORTC QLQ-BM22 data, together with sociodemographic and medical factors were analyzed by univariate analysis of variance (ANOVA). Items of significance were determined through backward selection, which were then put through multivariate analysis to determine further significance. RESULTS: Through ANOVA analysis, KPS>80 and breast primary histology were predictive of better HRQOL in the PS scale, while KPS>80, female gender, and breast primary histology were predictive of better HRQOL in the PC and FI scales. KPS>80 and prostate primary histology were predictive of better HRQOL in the PA scale. KPS>80 and primary cancer site were confirmed as significant predictive factors in multivariate analysis. RECOMMENDATIONS: This study identified baseline factors of gender, performance status, and primary histology as determinants of HRQOL in patients with bone metastases. Further study focusing on current treatment (chemotherapy, bisphosphonates, and radiotherapy) and spiritual well-being may identify additional factors affecting HRQOL. Understanding the influence of these factors will allow health care professionals to provide more effective palliative care.
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 | 0.000 | 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".