Comparison of the EuroQol and short form 6D in Singapore multiethnic asian knee osteoarthritis patients scheduled for total knee replacement
Bibliographic record
Abstract
OBJECTIVE: To compare the EuroQol (EQ-5D) and Short Form 6D (SF-6D) among multiethnic Asian patients with knee osteoarthritis (OA) scheduled for total knee replacement in Singapore. METHODS: Patients were asked to complete questionnaires including the EQ-5D, Short Form 36, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Lequesne knee index. EQ-5D and SF-6D utility scores were calculated using the scoring algorithms developed from the UK general population. Agreement between the 2 instruments was assessed by comparing their score distributions, means, medians, intraclass correlation coefficients (ICCs), and a Bland-Altman plot. Correlations of the EQ-5D and SF-6D with WOMAC and Lequesne knee index scores were also examined. RESULTS: A consecutive sample of 258 knee OA patients (127 English-speaking and 131 Chinese-speaking) participated. The mean +/- SD EQ-5D utility score was 0.49 +/- 0.31 (range -0.25-1.00) and the mean SF-6D utility score was 0.63 +/- 0.12 (range 0.32-0.89). In a hypothetical example, this 0.14-point difference in mean utility scores yielded a difference of $10,000/quality-adjusted life year (QALY) in cost-effectiveness ratios. The score distribution was bimodal for the EQ-5D and normal for the SF-6D. This poor agreement was also demonstrated by the Bland-Altman plot and the low ICC (range 0.18-0.54). Correlations of the WOMAC and Lequesne index with the EQ-5D were higher than with the SF-6D. CONCLUSION: Using different preference-based health-related quality of life instruments may yield different utility scores, which could have a great impact on QALY estimates. This highlights the importance of selecting appropriate instruments for economic evaluation. Additional research is needed to determine which instrument (the EQ-5D or the SF-6D) should be used in OA patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".