A view from the bridge: agreement between the SF‐6D utility algorithm and the Health Utilities Index
Bibliographic record
Abstract
BACKGROUND: The SF-6D is a new health state classification and utility scoring system based on 6 dimensions ('6D') of the Short Form 36, and permits a "bridging" transformation between SF-36 responses and utilities. The Health Utilities Index, mark 3 (HUI3) is a valid and reliable multi-attribute health utility scale that is widely used. We assessed within-subject agreement between SF-6D utilities and those from HUI3. METHODS: Patients at increased risk of sudden cardiac death and participating in a randomized trial of implantable defibrillator therapy completed both instruments at baseline. Score distributions were inspected by scatterplot and histogram and mean score differences compared by paired t-test. Pearson correlation was computed between instrument scores and also between dimension scores within instruments. Between-instrument agreement was by intra-class correlation coefficient (ICC). RESULTS: SF-6D and HUI3 forms were available from 246 patients. Mean scores for HUI3 and SF-6D were 0.61 (95% CI 0.60-0.63) and 0.58 (95% CI 0.54-0.62) respectively; a difference of 0.03 (p<0.03). Score intervals for HUI3 and SF-6D were (-0.21 to 1.0) and (0.30-0.95). Correlation between the instrument scores was 0.58 (95% CI 0.48-0.68) and agreement by ICC was 0.42 (95% CI 0.31-0.52). Correlations between dimensions of SF-6D were higher than for HUI3. CONCLUSIONS: Our study casts doubt on the whether utilities and QALYs estimated via SF-6D are comparable with those from HUI3. Utility differences may be due to differences in underlying concepts of health being measured, or different measurement approaches, or both. No gold standard exists for utility measurement and the SF-6D is a valuable addition that permits SF-36 data to be transformed into utilities to estimate QALYs. The challenge is developing a better understanding as to why these classification-based utility instruments differ so markedly in their distributions and point estimates of derived utilities.
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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 | 0.055 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".