How to Tackle Therapeutic Inertia in Heart Failure with Reduced Ejection Fraction. A Scientific Statement of the Heart Failure Association of the ESC
Bibliographic record
Abstract
Guideline-directed medical therapy (GDMT) in patients with heart failure and reduced ejection fraction (HFrEF) reduces morbidity and mortality, but its implementation is often poor in daily clinical practice. Barriers to implementation include clinical and organizational factors that might contribute to clinical inertia, i.e. avoidance/delay of recommended treatment initiation/optimization. The spectrum of strategies that might be applied to foster GDMT implementation is wide, and involves the organizational set-up of heart failure care pathways, tailored drug initiation/optimization strategies increasing the chance of successful implementation, digital tools/telehealth interventions, educational activities and strategies targeting patient/physician awareness, and use of quality registries. This scientific statement by the Heart Failure Association of the ESC provides an overview of the current state of GDMT implementation in HFrEF, clinical and organizational barriers to implementation, and aims at suggesting a comprehensive framework on how to overcome clinical inertia and ultimately improve implementation of GDMT in HFrEF based on up-to-date evidence.
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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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| 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".