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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".