Quality of life measurement in bone metastases: A literature review
Bibliographic record
Abstract
Quality of life measurement in bone metastases: A literature review Sukirtha Tharmalingam, Edward Chow, Kristin Harris, Amanda Hird, Emily SinclairRapid Response Radiotherapy Program, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, CanadaAbstract: Quality of life (QOL) has become an important consideration in the care of patients with bone metastases as prevalence, incidence and patient survival are on the rise. As a result, more interventional studies now measure patient’s QOL as a meaningful endpoint. However, well-developed bone metastases specific quality of life instruments are lacking. A literature review was conducted to better understand the nature of QOL instruments used in bone metastases trials. A total of 47 articles evaluating QOL in patients with bone metastases were identified. Twenty-five different instruments were used to evaluate QOL with study-designed questionnaires and the EORTC QLQ-C30 being most commonly employed. Many studies used more than one scale or instrument to measure QOL. This makes it difficult to compare QOL in bone metastases patients across studies and come to any formal conclusions. Therefore, this review demonstrates the need to develop a bone module that can be used across countries in future clinical trials.Keywords: bone metastases, quality of life, QOL instrument, review
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".