Who Should do What? Comparing EQ-5D-3L and EQ-5D-5L Proxy-ratings by Informal Caregivers and Health Professional for People with Dementia
Bibliographic record
Abstract
Abstract Background: Assessing health-related quality of life (HRQoL) among persons with dementia poses several challenges due to cognitive decline and limited perception. As a result, proxy ratings by family members or health professionals are used. The EQ-5D is the most commonly used generic and preference-based HRQoL instrument. Methodological drawbacks of the three-level version (EQ-5D-3L) prompted the development of the five-level version (EQ-5D-5L) by expanding the range in the domains. However, no comparison of the psychometric properties of both versions and different proxy ratings exist so far. Therefore, the objective of this study was to compare the psychometric properties of the EQ-5D-5L and EQ-5D-3L by application of different proxy ratings in dementia.Methods: The EQ-5D-3L and -5L were completed by n=52 family caregivers and one care-manager at baseline and three and six months later. In total, 106 caregiver and 133 care manager proxy ratings were completed. The EQ-5D-3L and 5L were evaluated in terms of acceptability, agreement, ceiling effects, redistribution properties and inconsistency, informativity (Shannon, H', and Shannon Evenness, J', indices) as well as convergent and discriminative validity. Results: Mean proxy index scores were higher for the 5L than the 3L. Missing values occurred less frequently in both proxy ratings and versions (<1%). Agreement between both measures was high but higher in caregiver than care-manager ratings (ICC 0.885 vs. 0.840). The relative ceiling effect decreased for the 3L compared to 5L by 56% in the caregiver- and 75% in the care-manager-rating. Inconsistency between both versions was low. Informativity increased from the 3L to the 5L version in both proxy ratings. The 5L also demonstrated a better discriminative ability than the 3L, especially in the caregiver rating. Compared with the 3L, the 5L had better convergent validity, especially for caregiver ratings.Conclusion: The EQ-5D-5L had high feasibility and acceptability and was superior by a reduction of ceiling effects and an improvement in informativity, discriminative ability and convergent validity. Proxy ratings by informal caregivers demonstrated better psychometric properties than professional care-manager ratings.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".