Comparing the performance of the EQ-5D and SF-6D when measuring the benefits of alleviating knee pain
Bibliographic record
Abstract
OBJECTIVE: To assess the practicality, validity and responsiveness of using each of two utility measures (the EQ-5D and SF-6D) to measure the benefits of alleviating knee pain. METHODS: Participants in a randomised controlled trial, which was designed to compare four different interventions for people with self-reported knee pain, were asked to complete the EQ-5D, SF-6D, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at both pre- and post-intervention. For both utility measures, we assessed their practicality (completion rate), construct validity (ability to discriminate between baseline WOMAC severity levels), and responsiveness (ability to discriminate between three groups: those whose total WOMAC score, i) did not improve, ii) improved by <20%, and iii) improved by > or = 20%). RESULTS: The EQ-5D was completed by 97.7% of the 389 participants, compared to 93.3% for the SF-6D. Both the EQ-5D and SF-6D were able to discriminate between participants with different levels of WOMAC severity (p < 0.001). The mean EQ-5D change was -0.036 for group i), 0.091 for group ii), and 0.127 for group iii), compared to 0.021, 0.023 and 0.053 on the SF-6D. These change scores were significantly different according to the EQ-5D (p < 0.001), but not the SF-6D. CONCLUSION: The EQ-5D and SF-6D had largely comparable practicality and construct validity. However, in contrast to the EQ-5D, the SF-6D could not discriminate between those who improved post-intervention, and those who did not. This suggests that it is more appropriate to use the EQ-5D in future cost-effectiveness analyses of interventions which are designed to alleviate knee pain.
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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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".