Validation of a novel diabetic retinopathy utility index using discrete choice experiments
Bibliographic record
Abstract
BACKGROUND/AIMS: To validate a preference-based Diabetic Retinopathy Utility Index (DRU-I) using discrete choice experiment (DCE) methods and assess disutilities associated with vision-threatening DR (VTDR: severe non-proliferative DR, proliferative DR and clinically significant macular oedema) and associated vision impairment. METHODS: The DRU-I comprises five quality-of-life dimensions, including Visual symptoms, Activity limitation/mobility, Lighting and glare, Socio-emotional well-being and Inconvenience, each rated as no, some, or a lot of difficulty. The DRU-I was developed using a DCE comprising six blocks of nine choice sets which, alongside the EuroQoL-5D (EQ-5D-3L) and Vision and Quality of Life (VisQoL) utility instruments, were interviewer-administered to participants. To ensure the DRU-I was sensitive to severe disease, we oversampled patients with VTDR. Data were analysed using conditional logit regression. RESULTS: Of the 220 participants (mean±SD age 60.1±11.3 years; 70.9% men), 57 (29.1%) and 139 (70.9%) had non-VTDR and VTDR, respectively, while 157 (71.4%), 20 (9.4%) and 37 (17.3%) had no, mild or moderate/severe vision impairment, respectively. Regression coefficients for all dimensions were ordered as expected, with worsening levels in each dimension being less preferred (theoretical validity). DRU-I utilities decreased as DR severity (non-VTDR=0.87; VTDR=0.80; p=0.021) and better eye vision impairment (none=0.84; mild=0.78; moderate/severe=0.72; p=0.012) increased. DRU-I utilities had low (r=0.39) and moderate (r=0.58) correlation with EQ-5D and VisQoL utilities, respectively (convergent validity). DISCUSSION: The DRU-I can estimate utilities associated with vision-threatening DR and associated vision impairment. It has the potential to assess the cost-effectiveness of DR interventions from a patient perspective and inform policies on resource allocation relating to DR.
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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 | 0.046 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".