A new SF-6Dv2 value set based on a hybrid model using SG, cTTO, and DCE data
Bibliographic record
Abstract
OBJECTIVE: To develop a value set for the Short-Form 6-Dimension version 2 (SF-6Dv2) by incorporating societal preferences obtained from three distinct approaches: Standard Gamble (SG), composite Time Trade-Off (cTTO), and Discrete Choice Experiment (DCE). METHODS: Data were gathered from the general population of Quebec, Canada, using the standardized valuation protocol developed by EuroQol for the cTTO and DCE tasks, as well as the valuation protocol developed by Sheffield University for the SG. The SG and cTTO data were analyzed using OLS, GLS, GLS Tobit, and heteroskedastic Tobit models. Conditional logit model was used for the DCE, while hybrid, hybrid Tobit, and heteroskedastic hybrid were applied to analyze the combined data from SG, cTTO, and DCE. The performance of models was assessed using mean absolute error (MAE), the logical consistency of the parameters, and significance levels. RESULTS: Over 56,000 observations collected from the SG, cTTO, and DCE were analyzed. The utility values generated by DCE were generally lower than those provided by cTTO and SG. Among the models tested, the heteroskedastic hybrid model demonstrated the best fit in terms of logical consistency and statistically significant coefficients. This model generated a value set ranging from -0.216 for the worst health state (555655) to 1 for full health (111111), with 0.52% of the values being negative and a MAE of 0.281. Among dimensions, the largest decrements were consistently found in the pain dimension, highlighting its significant impact on overall health state valuations. CONCLUSION: A heteroskedastic hybrid model using data from SG, cTTO, and DCE was identified as the most effective approach for generating the SF-6Dv2 value set and is expected to provide key input for healthcare decision-making.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".