Mapping the ADDQoL to the EQ-5D-5L and SF-6Dv2 among Chinese patients with type 2 diabetes mellitus
Bibliographic record
Abstract
The Audit of Diabetes-Dependent Quality of Life (ADDQoL) is a widely used instrument for assessing quality of life in Type 2 Diabetes Mellitus (T2DM). However, it does not directly yield health utility values essential for economic evaluations. This study developed mapping algorithms to predict EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores in T2DM patients in China. Cross-sectional data from 800 T2DM patients in China, stratified by age, sex, and geographical region, were divided into development (80%) and validation (20%) groups. Pearson correlation analyses were conducted to assess the conceptual overlap between ADDQoL and the EQ-5D-5L and SF-6Dv2. Six predictor sets and six regression methods were explored to map ADDQoL scores to EQ-5D-5L and SF-6Dv2 utility values, respectively. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), and intraclass correlation coefficient (ICC). For the development group, the mean (SD) ADDQoL Average Weighted Impact (AWI) score was − 2.426 (1.052), and the mean (SD) utility values for EQ-5D-5L and SF-6Dv2 were 0.928 (0.092) and 0.791 (0.133), respectively. Among all 36 alternative mapping models each for EQ-5D-5L and SF-6Dv2, the best performance was consistently observed in the two-part models that included the ADDQoL AWI, the first overview item, and their squared terms. For the algorithm mapping to EQ-5D-5L utility values, it achieved a MAE of 0.067, a RMSE of 0.095, and an ICC of 0.414; For the algorithm mapping to SF-6Dv2 utility values, the corresponding metrics were an MAE of 0.099, an RMSE of 0.120, and an ICC of 0.517. This study provides a mapping framework to estimate EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores. These algorithms could be used to support economic evaluations, specifically tailored for Chinese T2DM populations. The Audit of Diabetes-Dependent Quality of Life (ADDQoL) is a widely recognized, disease-specific instrument for assessing the quality of life in individuals with Type 2 Diabetes Mellitus (T2DM). While despite its widespread use, the ADDQoL does not directly produce health utility values, which are critical for economic evaluations in healthcare decision-making. This study developed mapping algorithms to predict EQ-5D-5L and SF-6Dv2 utility values from ADDQoL scores among T2DM patients in China.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".