Validity and responsiveness of EuroQol-5 dimension (EQ-5D) versus Short Form-6 dimension (SF-6D) questionnaire in chronic pain
Bibliographic record
Abstract
BACKGROUND: Assessments of health-related quality of life and particularly utility values are important components of health economic analyses. Several instruments have been developed to measure utilities. However no consensus has emerged regarding the most appropriate instrument within a therapeutic area such as chronic pain. The study compared two instruments - EQ-5D and SF-6D - for their performance and validity in patients with chronic pain. METHODS: Pooled data from three randomised, controlled clinical trials with two active treatment groups were used. The included patients suffered from osteoarthritis knee pain or low back pain. Differences between the utility measures were compared in terms of mean values at baseline and endpoint, Bland-Altman analysis, correlation between the dimensions, construct validity, and responsiveness. RESULTS: The analysis included 1977 patients, most with severe pain on the Numeric Rating Scale. The EQ-5D showed a greater mean change from baseline to endpoint compared with the SF-6D (0.43 to 0.58 versus 0.59 to 0.64). Bland-Altman analysis suggested the difference between two measures depended on the health status of a patient. Spearmans rank correlation showed moderate correlation between EQ-5D and SF-6D dimensions. Construct validity showed both instruments could differentiate between patient subgroups with different severities of adverse events and analgesic efficacies but larger differences were detected with the EQ-5D. Similarly, when anchoring the measures to a disease-specific questionnaire - Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) - both questionnaires could differentiate between WOMAC severity levels but the EQ-5D showed greater differences. Responsiveness was also higher with the EQ-5D and for the subgroups in which improvements in health status were expected or when WOMAC severity level was reduced the improvements with EQ-5D were higher than with SF-6D. CONCLUSIONS: This analysis showed that the mean EQ-5D scores were lower than mean SF-6D scores in patients with chronic pain. EQ-5D seemed to have higher construct validity and responsiveness in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".