Health state utilities for skeletal-related events secondary to bone metastases
Bibliographic record
Abstract
INTRODUCTION: Patients with bone metastases often experience skeletal-related events (SREs). Although cost-utility models are used to examine treatments for metastatic cancer, limited information is available on utilities of SREs. The purpose of this study was to estimate the disutility of four SREs: spinal cord compression, pathological fracture, radiation to bone, and surgery performed to stabilize a bone. METHODS: General population participants from the UK and Canada completed time trade-off (TTO) interviews to assess the utility of health states drafted based on literature review, clinician interviews, and patient interviews. Respondents first rated a health state describing cancer with bone metastases. Then, the SREs were added to this health state. RESULTS: Interviews were completed with 187 participants (50.8 % male, 80.2 % white). Cancer with bone metastases without an SRE had a mean utility of 0.47 (SD = 0.43) on a standard utility scale (1 = full health, 0 = death). Of the SREs, spinal cord compression was associated with the greatest disutility (i.e., the utility decrease): -0.32 with paralysis and -0.22 without paralysis. Surgery had a disutility of -0.07. Leg, arm, and rib fractures had disutilities of -0.06, -0.04, and -0.03. Two weeks of daily radiation treatment had a disutility of -0.06, while two radiation appointments had the smallest impact on utility (-0.02). CONCLUSION: All SREs were associated with statistically significant utility decreases, suggesting a perceived impact on quality of life beyond the impact of cancer with bone metastases. The resulting disutilities may be used in cost-utility models examining treatments to prevent SREs secondary to bone metastases.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".