Use of the U.S. and U.K. Scoring Algorithm for the EuroQol-5D in an Economic Evaluation of Cardiac Care
Bibliographic record
Abstract
BACKGROUND: Most studies that have used the EuroQol-5D instrument (EQ-5D) have used a scoring algorithm based on preferences solicited from the U.K. population. An algorithm recently was developed for the U.S. population, with studies showing meaningful differences in the results obtained using the 2 algorithms. We recently published an economic evaluation assessing the use of drug-eluting stents in patients undergoing percutaneous coronary intervention (PCI). OBJECTIVES: Using the aforementioned economic evaluation, we describe the EQ-5D utility scores resulting from use of U.S. and U.K. algorithms and explore the differences in the incremental cost-utility ratio (ICER) resulting from use of the different EQ-5D estimates. METHODS: EQ-5D data were obtained from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart (APPROACH) disease registry. Individual responses were scored once with each algorithm. The within-individual difference was calculated (U.S. score-U.K. score). The mean, SD, and range were compared using paired t tests. The resulting ICERs were compared using probabilistic sensitivity analysis. RESULTS: The U.K. mean was statistically different from the U.S. mean (0.83, SD 0.20 vs. 0.87, SD 0.15, P<0.001). The mean within individual difference was 0.04 with a wide range (-0.02 to +0.41). The resulting ICER are CAN $58,635 (95% confidence interval $198,248-$34,406) per quality-adjusted life year and CAN $58,229 (95% confidence interval $116,818-$38,779) per quality-adjusted life year for the U.K. and U.S. algorithms, respectively (P value: 0.07). CONCLUSIONS: The algorithms produce quite notable differences within individuals. The effect on the mean score is less pronounced. In the context of our economic evaluation, however, the impact of using the U.S. algorithm on the ICER is negligible.
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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 | 0.017 | 0.003 |
| 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".