Sensitivity to change of generic preference-based instruments (EQ-5D-3L, EQ-5D-5L, and HUI3) in the context of treatment for people with prescription-type opioid use disorder in Canada
Bibliographic record
Abstract
PURPOSE: Using data from a randomized controlled trial for treatment of prescription-type opioid use disorder in Canada, this study examines sensitivity to change in three preference-based instruments [EQ-5D-3L, EQ-5D-5L, and the Health Utilities Index Mark 3 (HUI3)] and explores an oft-overlooked consideration when working with contemporaneous responses for similar questions-data quality. METHODS: Analyses focused on the relative abilities of three instruments to capture change in health status. Distributional methods were used to categorize individuals as 'improved' or 'not improved' for eight anchors (seven clinical, one generic). Sensitivity to change was assessed using area under the ROC (receiver operating characteristics) curve (AUC) analysis and comparisons of mean change scores for three time periods. A 'strict' data quality criteria, defined a priori, was applied. Analyses were replicated using 'soft' and 'no' criteria. RESULTS: Data from 160 individuals were used in the analysis; 30% had at least one data quality violation at baseline. Despite mean index scores being significantly lower for the HUI3 compared with EQ-5D instruments at each time point, the magnitudes of change scores were similar. No instrument demonstrated superior sensitivity to change. While six of the 10 highest AUC estimates were for the HUI3, 'moderate' classifications of discriminative ability were identified in 12 (of 22) analyses for each EQ-5D instrument, compared with eight for the HUI3. CONCLUSION: Negligible differences were observed between the EQ-5D-3L, EQ-5D-5L, and HUI3 regarding the ability to measure change. The prevalence of data quality violations-which differed by ethnicity-requires further investigation.
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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 | 0.071 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".