Characterization of Health-Related Quality of Life in Heart Failure Patients with Preserved Versus Low Ejection Fraction in CHARM
Bibliographic record
Abstract
Abstract Background Limited comparative studies assessing the health-related quality of life (HRQL) in heart failure (HF) patients with preserved vs. low ejection fraction (LVEF) have been disparate. Aims The aims of this study were a) to characterize HRQL in a large population of HF patients with preserved and low LVEF and b) to determine the factors associated with worse HRQL. Methods Patients with symptomatic HF (NYHA Class II—IV) enrolled in the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) HRQL study completed the Minnesota Living with Heart Failure questionnaire at randomization. Patients were stratified into 2 HF cohorts: preserved LVEF (>40%) and low LVEF (≤40%). Results In 2709 of the eligible 2744 (98.6%) patients, the summary scores ranged from 0 to 105 (mean 40.9). There were no differences in overall responses of HF patients with preserved vs. low LVEF (41.1 vs. 40.8). Independent factors associated with worse HRQL in both populations included female gender, younger age, higher body mass index, lower systolic blood pressure, greater symptom burden, and worse functional status. Conclusions In symptomatic HF patients, HRQL is equally impaired in both preserved and low LVEF populations. Targeting improvement in symptoms and HRQL is an important treatment objective in all HF 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".