Health utility indexes in patients with acute coronary syndromes
Bibliographic record
Abstract
BACKGROUND: Acute coronary syndromes (ACS) have been associated with lower health utilities (HUs) compared with the general population. Given the prognostic improvements after ACS with the implementation of coronary angiography (eg, percutaneous coronary intervention (PCI)), contemporary HU values derived from patient-reported outcomes are needed. METHODS: We analysed data of 1882 patients with ACS 1 year after coronary angiography in a Swiss prospective cohort. We used the EuroQol five-dimensional questionnaire (EQ-5D) and visual analogue scale (VAS) to derive HU indexes. We estimated the effects of clinical factors on HU using a linear regression model and compared the observed HU with the average values of individuals of the same sex and age in the general population. RESULTS: Mean EQ-5D HU 1-year after coronary angiography for ACS was 0.82 (±0.16) and mean VAS was 0.77 (±0.18); 40.9% of participants exhibited the highest utility values. Compared with population controls, the mean EQ-5D HU was similar (expected mean 0.82, p=0.58) in patients with ACS, but the mean VAS was slightly lower (expected mean 0.79, p<0.001). Patients with ACS who are younger than 60 years had lower HU than the general population (<0.001). In patients with ACS, significant differences were found according to the gender, education and employment status, diabetes, obesity, heart failure, recurrent ischaemic or incident bleeding event and participation in cardiac rehabilitation (p<0.01). CONCLUSIONS: At 1 year, patients with ACS with coronary angiography had HU indexes similar to a control population. Subgroup analyses based on patients' characteristics and further disease-specific instruments could provide better sensitivity for detecting smaller variations in health-related quality of life.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".