Development and validation of a diabetes-specific health state classification system and valuation function based on the multi-attribute theory
Bibliographic record
Abstract
Preference-Based Measures of Health (PBMH) provide 'preference' or 'utility' weights that enable the calculation of quality-adjusted life years for the economic evaluations of interventions. The Diabetes Utility Index (DUI) was developed as a two-page, self-administered diabetes-specific PBMH that can replace expensive time-consuming interviews with patients to estimate their health state utilities. Inputs from theory, an existing diabetes-specific measure of quality of life, and statistical analyses were submitted to a clinical expert panel. After three rounds of pilot surveys (n1=52, n2=65, n3=111) at primary care clinics in Morgantown, WV, five attributes and severity categories for each attribute were finalized on the basis of the results of Rasch Analysis and consultations with the panel. The final attributes were: 'Physical Ability & Energy', 'Relationships', 'Mood & Feelings', 'Enjoyment of Diet', and 'Satisfaction with Management of diabetes'. The next step involved obtaining preferences for health states based on combinations of DUI attributes and severity levels from 100 individuals with diabetes, recruited from primary care and community settings in and around Morgantown, WV, in hour-long one-on-one interviews. These health states were anchor states, single-attribute level states including corner states, and marker states. The interviews provided data to calculate a Multi-Attribute Utility Function (MAUF) that calculates utilities for any of the 768 health states that can be defined by the DUI, on a scale where 1.00=Perfect Health and 0.00=the all worse 'Pits' state, from respondents' answers to its five questions. In addition to an overall index score, attribute-level preference scores were also calculable by the function. Finally, a validation survey was conducted in collaboration with the West Virginia University (WVU) Diabetes Institute. For concurrent and construct validation purposes, the DUI was mailed to individuals with diabetes along with generic PBMH like the EuroQol EQ-5D, the SF-6D and other patient-reported outcomes measures like the Diabetes Symptoms Checklist-Revised, the Short Form 12 (SF-12) and the Well-Being Questionnaire (W-BQ12), and their surveys responses (n=396) were merged with a clinical database consisting of ICD-9 diagnosis codes. The DUI utilities were found to be largely free of socio-demographic effects and its scores were well distributed between 0.00 and 1.00. The DUI moderately correlated with generic PBMH and distinguished between severity groups based on diabetes symptoms and complications. The scoring function of the DUI calculated utilities favorably compared against cardinal Standard Gamble utilities obtained directly from patients for three DUI health states. These results show evidence of the feasibility and validity of
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".