European Society of Cardiology Quality Indicators Update for the Care and Outcomes of Adults with Heart Failure. The Heart Failure Association of the ESC
Bibliographic record
Abstract
AIMS: To update the European Society of Cardiology (ESC) quality indicators (QIs) for the evaluation of the care and outcomes of adults with heart failure. METHODS AND RESULTS: The Working Group comprised experts in heart failure including members of the ESC Clinical Practice Guidelines Task Force for heart failure, members of the Heart Failure Association, and a patient representative. We followed the ESC methodology for QI development. The 2023 focused guideline update was reviewed to assess the suitability of the recommendations with strongest association with benefit and harm against the ESC criteria for QIs. All the new proposed QIs were individually graded by each panellist via online questionnaires for both validity and feasibility. The existing heart failure QIs also underwent voting to 'keep', 'remove' or 'modify'. Five domains of care for the management of heart failure were identified: (1) structural QIs, (2) patient assessment, (3) initial treatment, (4) therapy optimization, and (5) patient health-related quality of life. In total, 14 'main' and 3 'secondary' QIs were selected across the five domains. CONCLUSION: This document provides an update of the previously published ESC QIs for heart failure to ensure that these measures are aligned with contemporary evidence. The QIs may be used to quantify adherence to clinical practice as recommended in guidelines to improve the care and outcomes of patients with heart failure.
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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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".