Routine collection of EQ-5D-5L derived utility scores in a brain metastases clinic: Correlation with health-related quality of life (FACT-Br).
Bibliographic record
Abstract
200 Background: Brain metastasis is a common occurrence in many cancers. However, with new therapies (stereotactic radiotherapy) and longer survival due to improved systemic therapies, more contemporary estimates of health utility scores (HUS) are required for this specialized cancer population. Further, recent efforts have tried to incorporate routine collection of such data, especially in an era when new radiation and new systemic therapies, especially targeted therapies require such data when undergoing health technology assessments. Methods: In a cross-sectional study design, patients in the specialized brain metastases clinic at Princess Margaret Cancer Centre were approached to complete the Health-related quality of life (HRQoL) tool, FACT-Br, and the EQ-5D-5L (to derived HUS using Canadian reference values) on iPad or paper. In addition, consent was obtained to collect clinico-demographic data from the patient and chart. In addition to descriptive analysis and participation rates, HUS were correlated to FACT-Br and its subscales. Results: Of 204 eligible patients, 134 were recruited (66% participation rate) from May 2017- Feb 2018. Of 105 patients in this preliminary analysis, the median age was 60 (range: 25-94) years; 73% were female; 64% were Caucasian; 20% were Asian; 49% had lung, 15% had breast, 37% had other primary cancer; 81% received some form of radiotherapy. Median time from first treatment of brain metastasis to survey was 12 (range 0.03-100) months. There were correlations with the following FACT-Br subscales: physical well-being (WB) (rho = 0.70), emotional WB (rho = 0.42), social WB (rho = 0.25), functional WB (rho = 0.59), and brain-specific subscale (rho = 0.67); overall FACT-Br (rho = 0.73); all correlations were p < 0.001. Conclusions: Patients with brain metastasis were generally willing to complete EQ-5D-5L, and patients had good HUS. HUS as measured indirectly by EQ-5D-5L were correlated to HRQoL as measured by FACT-Br or its subscales, with high correlation to the overall FACT-Br. EQ-5D-5L is a practical and useful tool to assess HUS routinely in brain metastases patients.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".