The HeartQoL: Part I. Development of a new core health-related quality of life questionnaire for patients with ischemic heart disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it