Using Instrument-Defined Health State Transitions to Estimate Minimally Important Differences for Four Preference-Based Health-Related Quality of Life Instruments
Bibliographic record
Abstract
OBJECTIVE: To estimate minimally important differences (MIDs) for the EQ-5D, Health Utilities Index Mark II (HUI2), HUI3, and SF-6D health index scores using health-state transitions defined by each instrument's multiattribute health classification (MAHC) system. METHODS: We assume that changes in preference scores associated with the smallest health transitions defined by an MAHC system are minimally important. Any transitions between 2 health states defined by an MAHC system which differ in only one health dimension or attribute and by only one functional level are considered "smallest health transitions." Thus, each such health transition provides 1 MID estimate. The MID for each of the 4 instruments was estimated using all the hypothetical smallest health transitions defined by its MAHC system. RESULTS: Based on our definitions, the total number of smallest health transitions was 405 for the EQ-5D, 127,600 for the HUI2, 6,382,800 for the HUI3, and 86,700 for the SF-6D. The mean (standard deviation) MID estimate was 0.040 (0.026) for the EQ-5D (US algorithm), 0.082 (0.032) for the EQ-5D (UK algorithm), 0.045 (0.039) for the HUI2, 0.032 (0.027) for the HUI3, and 0.027 (0.028) for the SF-6D. The effect sizes of these MID estimates ranged from 0.11 to 0.37. These MID estimates are quite comparable to published values estimated from empirical data using anchor-based methods. CONCLUSIONS: It is possible to use health transitions defined by the MAHC system to estimate the MIDs for preference-based health index scores. This study provides new information regarding MID estimates for the 4 health indices examined.
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.001 | 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".