Differences in health utilities between cancer patients and the general population: The case of Quebec using the SF-6Dv2
Bibliographic record
Abstract
A considerable debate persists in the literature about whose preferences should be considered in the calculation of quality-adjusted life-years. Some suggest considering only the preferences of the general population, while others advocate for the consideration of those of patients or a combination of both. This study aims to inform and measure the differences in health preferences between cancer patients and the general population in Quebec. A total of 60,976 observations representing the preferences of the general population for various health states were collected and used to develop a new value set using the SF-6Dv2. This value set was generated by combining 34,299 observations with time trade-off (TTO) and 26,677 observations with discrete choice experiment (DCE). Utility scores derived from this value set were compared to those of patients' preferences from a new value set in breast and colorectal patients for the SF-6Dv2. For both patients and the general population, the 'Pain' dimension was the highest contributor to the utility score. However, noticeable differences were observed in the estimates. Estimates of levels 2 and 3 were generally lower for cancer patients, while they were more likely to have greater estimates in severe levels. Significant differences in utility scores were also noticed with the general population showing higher mean utility scores for the same health states. These differences increased as the health states worsened. This study sheds light on the existing differences in preferences between cancer patients and the general population of Quebec for a better consideration in healthcare decision-making.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".