Mapping between Visual Analogue Scale and Standard Gamble data; results from the UK Health Utilities Index 2 valuation survey
Bibliographic record
Abstract
We examine the relationship between Visual Analogue Scale (VAS) and Standard Gamble (SG) assumed in the development of the multiplicative multi-attribute utility functions (M-MAUFs) for the Health Utilities Index (HUI) Mark 2 and Mark 3, using data from a UK valuation study of the HUI2. A range of functional forms are considered, and are compared on the basis of their explanatory power and predictive ability.A restricted cubic function fits the data better than a power curve with a mean absolute error (MAE) of 0.025 and root mean square error (RMSE) of 0.029 compared to a MAE of 0.135 and RMSE of 0.135 for the power curve. The use of a cubic mapping function instead of a power function leads to different predicted health state values. We question the reliance on the assumption of a power curve relationship between VAS and SG data, in the Health Utilities Index valuation framework. Our results demonstrate that further work is required to examine the appropriateness of the published M-MAUFs for the Health Utilities Indices.
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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 | 0.012 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".