Comparing the EQ-5D 3L and 5L: measurement properties and association with chronic conditions and multimorbidity in the general population
Bibliographic record
Abstract
BACKGROUND: Studies comparing the measurement properties of EQ-5D 3L (3L) and EQ-5D 5L (5L) are limited to specific patient populations with small sample sizes. Using a general population sample, we compared 3L and 5L in terms of their measurement properties and association with number of chronic conditions, including multimorbidity--the concurrent occurrence of two or more chronic conditions. METHODS: Data were available from two consecutive cycles of a cross-sectional telephone interview survey using 3L (2010 cycle) and 5L (2012 cycle), in the general population of adults (age ≥ 18 years) in Alberta, Canada. Measurement properties were compared by determining their feasibility, ceiling effect, and discriminatory power (Shannon indices) for 3L and 5L. Linear regression models were fitted to test the associations between multimorbidity and EQ-5D index score. RESULTS: Data were available for 4946 (2010) and 4752 (2012) survey respondents with information on HRQL. Compared to 3L, 5L showed lower ceiling effect (32.3% versus 42.1%), higher absolute discriminatory power (Shannon index, mean 0.79 versus 0.52) and higher relative discriminatory power (Shannon Evenness index, mean 0.09 versus 0.06 for 3L). Despite these differences, similar relationships of lower HRQL with greater multimorbidity were observed for the 3L (ß = -0.13, 95% CI -0.15; -0.11) and 5L (ß = -0.12, 95% CI -0.13; -0.11). CONCLUSIONS: Using a general population sample, the EQ-5D 5L showed better measurement properties than the EQ-5D 3L. Nonetheless, clinically important differences in HRQL associated with multimorbidity were similar in magnitude using both versions of EQ-5D.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".