A Time Trade-off-derived Value Set of the EQ-5D-5L for Canada
Bibliographic record
Abstract
BACKGROUND: The 5-level version of the EQ-5D (EQ-5D-5L) was recently developed. A number of preference-based scoring systems are being developed for several countries around the world. OBJECTIVE: To develop a value set for the EQ-5D-5L based on societal preferences in Canada. METHODS: We used age, sex, and education quota sampling from the general population from 4 cities across Canada. Composite time trade-off (cTTO) and traditional time trade-off (tTTO) were used as the main elicitation technique. A total of 86 EQ-5D-5L health states grouped into 10 blocks were valued using cTTO, whereas a subset of 18 severe states was also valued using tTTO. Participants meeting predefined inconsistency criteria were excluded from the analyses. For the value set development, we used tTTO and positive cTTO values, while censoring negative and zero cTTO values at zero. Models with the main effects presented using linear terms combined with various additional terms were estimated. The preferred model was selected based primarily on logically ordered coefficients, and secondly model fit. RESULTS: Of the 1209 participants who completed the interview, 136 met criteria that excluded them from the primary analyses. The demographics and socioeconomic status of the remaining 1073 participants were similar to the Canadian general population. The preferred model has 5 linear terms for the main effects, a term for level 4 or 5 for each dimension, and a term for the squared total number of level 4 or 5 beyond the first. For this preferred model, the health utilities ranged from -0.148 for the worst (55555) to 0.949 for the best (11111) EQ-5D-5L states. CONCLUSIONS: This is the first TTO-based value set of the EQ-5D-5L for Canada. It can be used to support the health utility estimation in economic evaluations for reimbursement decision making in Canada.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".