Estimation of an Instrument-Defined Minimally Important Difference in EQ-5D-5L Index Scores Based on Scoring Algorithms Derived Using the EQ-VT Version 2 Valuation Protocols
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To estimate and compare the minimally important difference (MID) in index score of country-specific EQ-5D-5L scoring algorithms developed using EuroQol Valuation Technology protocol version 2, including algorithms from Germany, Indonesia, Ireland, Malaysia, Poland, Portugal, Taiwan, and the United States. METHODS: A simulation-based approach contingent on all single-level transitions defined by the EQ-5D-5L descriptive system was used to estimate the MID for each algorithm. RESULTS: The resulting mean (and standard deviation) instrument-defined MID estimates were Germany, 0.083 (0.022); Indonesia, 0.093 (0.012); Ireland, 0.098 (0.023); Malaysia, 0.072 (0.010); Poland, 0.080 (0.030); Portugal, 0.080 (0.018); Taiwan, 0.101 (0.010); and the United States, 0.078 (0.014). CONCLUSIONS: These population preference-based MID estimates and accompanying evidence of how such values vary as a function of baseline index score can be used to aid interpretation of index score change. The marked consistency in the relationship between the calculated MID estimate and the range of the EQ-5D-5L index score, represented by a ratio of 1:20, might substantiate a rule of thumb allowing for MID approximation in EQ-5D-5L index score warranting further investigation.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it