Developing a Valuation Function for the Preference-Based Multiple Sclerosis Index: Comparison of Standard Gamble and Rating Scale
Bibliographic record
Abstract
OBJECTIVE: The standard gamble (SG) and rating scale (RS) are two approaches that can be employed to elicit health state preferences from patients in order to inform decision making. The objectives of this study were: (i) to contribute evidence towards the similarities and differences in the SG and the RS to reflect patient preferences, and (ii) to develop a multi-attribute utility function (MAUF) (i.e., scoring algorithm) for the PBMSI. STUDY DESIGN: Two samples were recruited for the study. The first sample provided cross-sectional data to generate the preference weights which were then used to develop (D) the MAUFD. The distribution of SG and RS were compared across levels of perceived difficulty. The second sample provided additional data to validate (V) the MAUF, termed MAUFV. RESULTS: The mean RS values ranged from 0.39 to 0.65, whereas the mean SG values were much higher ranging from 0.80 to 0.91. Correlations between the two methods were very low ranging from -0.29 to 0.15. Bland-Altman plots revealed the extent of differences in values produced by the two methods. CONCLUSION: In contemplating trade-offs in the selection of a preference-based elicitation approach for a MAUF that could guide clinical decision making, results suggest the RS is preferable in terms of feasibility and validity for MS patients. The PBMSI with patient preferences shows promise as a measure of health-related quality of life for MS.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".