Comparison of EQ-5D and 15D instruments for assessing the health-related quality of life in cardiac surgery patients
Bibliographic record
Abstract
AIMS: Patient-centred outcomes can be measured with different instruments. We compared the performance of two health-related quality-of-life (HRQoL) measures, EQ-5D and 15D, in patients undergoing elective coronary artery bypass grafting (CABG). METHODS AND RESULTS: Patients who were admitted for elective CABG in Kuopio University Hospital Finland in 2012-14 and had completed both instruments concurrently as part of the admission process (n = 182). Follow-up was conducted by postal survey 12 months after the CABG operation. The validity, agreement, and responsiveness to change of both instruments were examined. The mean baseline HRQoL index scores obtained by the EQ-5D and the 15D were 0.795 and 0.859, respectively (P < 0.001 for difference). The agreement between instruments was poor (Spearman's rho = 0.449; P < 0.001). Observed ceiling effects at baseline for the EQ-5D and 15D were 31.9 and 4.4%, respectively. EQ-5D was able to discriminate distinct Canadian Cardiovascular Society groups. During the 1-year follow-up, clinically important improvement was observed in 39.6 and 53.3% of patients with the EQ-5D and the 15D, respectively. However, with the 15D, the number of operated patients required to produce one additional quality-adjusted life year (QALY) was more than twice as high compared with the EQ-5D. CONCLUSION: EQ-5D and 15D do not appear to be interchangeable when patient-centred outcomes in CABG patients are assessed. The EQ-5D seems to have better discriminative power and known-group validity, whereas the 15D is more sensitive to change over time. These instruments lead to significantly different estimates concerning the number of QALYs gained.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".