Canadian cancer site-specific health utility values: Creating the basis for measuring value and costs of therapy.
Bibliographic record
Abstract
7 Background: Health utility values (HUVs) play an integral role when conducting health economic analyses, but a paucity of reference HUVs exists for cancer patients. Using EQ-5D, we generated reference HUVs for multiple malignancies. We further assessed patient willingness to compete the instrument on a regular basis by adding the EQ-5D to an Ontario-wide patient-reported symptom tool mandated by Cancer Care Ontario, the provincial cancer government agency. Methods: 1,831 cancer patients across all non-CNS solid and hematologic cancer sites at the Princess Margaret Cancer Centre completed the EQ-5D instrument; a subset (n=618) were asked about the acceptability of regularly completing the EQ-5D. HUVs were calculated using Canadian valuations. Results: The mean±SD HUV for all patients was 0.81±0.15, but were significantly different across different disease sites (p<0.0001): Testicular cancer, 0.87±0.13; prostate, 0.87±0.15; colorectal, 0.83±0.12; head/neck, 0.82±0.15; lymphoma, 0.82±0.15; breast, 0.81±0.17; esophageal, 0.81±0.16; ovarian, 0.79±0.15; leukemia, 0.78±0.15; lung, 0.78±0.13 and myeloma, 0.77±0.14. Confirming the validity of these HUVs, patients with PRO-ECOG scores of 0, 1, 2 and 3 had HUVs of 0.90±0.14, 0.77±0.11, 0.65±0.14 and 0.59±0.19, respectively (p<0.0001). In patients with solid tumors, those with local disease had HUVs of 0.82±0.15; metastatic disease, 0.80±0.15; p=0.015. 88% of patients reported that the EQ-5D was easy to complete, 92% took less than 5 minutes, 89% were satisfied with its length and 86% were satisfied with the types of questions asked. Importantly, 92% reported that they would complete the EQ-5D, even if it was used solely for research purposes and 73% agreed with the notion of completing it regularly at their clinic visits. Conclusions: We present the first Canadian reference dataset of HUVs for common cancers; stage-and site-specific reference values will be presented at the meeting. Mean HUVs varied by disease site, performance status, and disease severity. Furthermore, a majority of patients surveyed were willing to complete the EQ-5D on a regular basis, suggesting that routine administration is feasible across Ontario.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".