Elicitation of Health Utilities in Oncology in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
PURPOSE: Health utilities (HUs) are quantitative measures of quality of life that are used to derive outcomes such as quality-adjusted life years in cost-effectiveness analyses. In the Kingdom of Saudi Arabia, there are no HUs for cancer. This study aimed to generate HU estimates for various health states associated with cancer in the Kingdom of Saudi Arabia. METHODS: Adult citizens of the Kingdom of Saudi Arabia, patients with cancer, and patients without cancer were recruited to participate in an online version of the Time Trade-Off (TTO) survey, a direct method that asks participants to indicate the amount of time they are willing to trade off in return for full health. The time horizon was 10 years. Patients were surveyed on their own health state; patients without cancer were presented with a scenario describing stage III colon cancer and were asked to act as proxies. RESULTS: Mean HU score was 0.398 (n = 398), 0.315 for patients with cancer (n = 199), and 0.482 for patients without cancer (n = 199). Among patients, the largest subgroup with colorectal cancer (n = 105), had a mean HU of 0.296; the subgroup with the lowest mean HU was patients with hepatocellular cancer (n = 3; 0.047), and the subgroup with the highest mean HU was patients with cholangiocarcinoma (n = 5; 0.508). Overall, the initial stage I subgroup (n = 7) had a mean HU of 0.456; initial stage II (n = 25), 0.240; stage II (n = 67), 0.319; and initial stage IV (n = 77), 0.320. CONCLUSION: To our knowledge, this is the first study of this size to elicit HU scores for cancer in the Kingdom of Saudi Arabia. Patients may have had clinically worse disease than the patients in the scenario that was presented to patients without cancer. Further analyses are warranted for specific types of cancer. These HUs can in turn be applied in cost-utility analyses.
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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 | 0.005 | 0.020 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".