Valuing the SF-6Dv2 Classification System in the United Kingdom Using a Discrete-choice Experiment With Duration
Bibliographic record
Abstract
OBJECTIVE: An updated version of the SF-6D Classification System (SF-6Dv2) has been developed, and utility value sets are required. The aim of this study was to test the development of a United Kingdom SF-6Dv2 value set, and address limitations of the existing SF-6D value set (which results in a narrow range of utilities). This was done using 2 discrete-choice experiment (DCE) tasks. Interactions and preference heterogeneity were also investigated. RESEARCH DESIGN AND SUBJECTS: An online sample of respondents (n=3014) completed 10 DCE with duration choice sets from an efficient design of 300 (Design 1) and 2 DCE with duration choice sets including immediate death from a set of 60 (Design 2). Conditional logit regression was used to estimate value set models with and without interactions. We investigated preference heterogeneity using latent class models. RESULTS: Models including ordered coefficients within each dimension were developed, with the favored model including an additional interaction term when one dimension was at the most severe level. Value sets differed across Designs 1 and 2. Design 1 models had a wider utility range and a higher proportion of negative values. The most important dimensions were pain, mental health, and physical functioning. Preference heterogeneity was apparent, with a 2-class model describing the data. CONCLUSIONS: We developed and applied a protocol to value the SF-6Dv2 using DCE. The results provide a provisional value set for use in resource allocation. The protocol can be applied internationally. Further work should investigate how to account for preference heterogeneity in value set production.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".