The HeartQoL: Part I. Development of a new core health-related quality of life questionnaire for patients with ischemic heart disease
Bibliographic record
Abstract
BACKGROUND: Evaluation of health-related quality of life (HRQL) is important in improving the quality of patient care. METHODS: The HeartQoL Project, with cross-sectional and longitudinal phases, was designed to develop a core ischemic heart disease (IHD) specific HRQL questionnaire, to be called the HeartQoL, for patients with angina, myocardial infarction (MI), or ischemic heart failure. Patients completed a battery of questionnaires and Mokken scaling analysis was used to identify items in the HeartQoL questionnaire. RESULTS: We enrolled 6384 patients (angina, n = 2111, 33.1%; MI, n = 2351, 36.8%; heart failure, n = 1922, 30.1%) across 22 countries and 15 languages. The HeartQoL questionnaire comprises 14-items with 10-item physical and 4-item emotional subscales which are scored from 0 (poor HRQL) to 3 (better HRQL) with a global score if needed. The mean baseline HeartQoL global score was 2.2 (±0.5) in the total group and was different (p < 0.001) by diagnosis (MI, 2.4 ± 0.5; angina, 2.2 ± 0.6; and heart failure, 2.1 ± 0.6). CONCLUSION: The HeartQoL questionnaire, with global and subscale scores, has the potential to allow clinicians and researchers to (a) assess baseline HRQL, (b) make between-diagnosis comparisons of HRQL, and (c) evaluate change in HRQL in patients with angina, MI, or heart failure with a single IHD-specific HRQL instrument.
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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 | 0.005 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".