Adherence to Guideline-Directed Medical Treatments in Heart Failure. A Scientific Statement of the Heart Failure Association (HFA) of the ESC and the ESC Working Group on Cardiovascular Pharmacotherapy
Bibliographic record
Abstract
Heart failure (HF) affects over 60 million individuals globally. Contemporary guideline-directed medical therapies (GDMT) reduce cardiovascular mortality and HF hospitalizations. However, medication non-adherence represents a critical barrier limiting real-world efficacy of GDMT. This scientific statement aims to provide a comprehensive framework for understanding, measuring, and addressing medication non-adherence in HF management across diverse healthcare settings. Addressing medication non-adherence requires systematic, multifaceted approaches targeting individual patient barriers while implementing system-level interventions. Polypills, digital monitoring platforms, enhanced patient education and empowerment, and multidisciplinary care models represent promising strategies to optimize therapeutic adherence and improve clinical outcomes in HF management.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".