Comparison of SF-6D and EQ-5D Scores in Patients With Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Utility values are a key component of a cost-utility analysis. The EQ-5D and SF-6D are two commonly used measures for deriving utilities. Of particular importance is assessing the performance of these instruments in terms of validity. OBJECTIVES: This study aimed to compare the performance of the EQ-5D and the SF-6D in different states of breast cancer. PATIENTS AND METHODS: This was a cross-sectional study of 163 patients with breast cancer who attended the breast cancer subspecialty clinic affiliated with the breast cancer research center (BCRC) at ACECR, in Tehran, Iran, and were consecutively recruited. Patients completed several questionnaires, including the EQ-5D, SF-36, and general questions regarding their demographic characteristics. Utility values for different states of breast cancer were obtained using predetermined algorithms for the EQ-5D and SF-6D. The distribution of the utility values and the differences between the different states for both instruments were statistically assessed. Furthermore, the agreement between the two instruments was evaluated using intra-class correlation coefficients and Bland-Altman plots. RESULTS: The mean and median EQ-5D utility scores for the total sample were 0.685 and 0.761, respectively. The mean SF-6D utility score for the total sample was 0.653, and the median utility score was 0.640. The mean utility values of the EQ-5D for "state P," "state R," "state S," and "state M" were estimated as 0.674, 0.718, 0.730, and 0.552, respectively. The SF-6D provided mean utility values of 0.638, 0.677, 0.681, and 0.587 for those states. Both instruments assigned statistically significant (P < 0.01) scores for different states. The intra-class correlation for the two measures was 0.677 (95% confidence interval (CI): 0.558 - 0.764). The Bland-Altman plot indicated a better agreement on the higher values and that at higher values, the EQ-5D yields a higher score than the SF-6D; this relationship was reversed at lower values. CONCLUSIONS: Although the two instruments were able to discriminate between various states, the values derived from these instruments were quite different. This distinction could have influenced the conclusions of an economic evaluation. Further research is required to determine which instrument should be used in economic evaluations.
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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 | 0.005 | 0.001 |
| 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) | 0.002 | 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 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".