Optimizing Outcomes in Heart Failure: 2022 and Beyond
Bibliographic record
Abstract
Although the development of therapies and tools for the improved management of heart failure (HF) continues apace, day-to-day management in clinical practice is often far from ideal. A Cardiovascular Round Table workshop was convened by the European Society of Cardiology (ESC) to identify barriers to the optimal implementation of therapies and guidelines and to consider mitigation strategies to improve patient outcomes in the future. Key challenges identified included the complexity of HF itself and its treatment, financial constraints and the perception of HF treatments as costly, failure to meet the needs of patients, suboptimal outpatient management, and the fragmented nature of healthcare systems. It was discussed that ongoing initiatives may help to address some of these barriers, such as changes incorporated into the 2021 ESC HF guideline, ESC Heart Failure Association quality indicators, quality improvement registries (e.g. EuroHeart), new ESC guidelines for patients, and the universal definition of HF. Additional priority action points discussed to promote further improvements included revised definitions of HF 'phenotypes' based on trial data, the development of implementation strategies, improved affordability, greater regulator/payer involvement, increased patient education, further development of patient-reported outcomes, better incorporation of guidelines into primary care systems, and targeted education for primary care practitioners. Finally, it was concluded that overarching changes are needed to improve current HF care models, such as the development of a standardized pathway, with a common adaptable digital backbone, decision-making support, and data integration, to ensure that the model 'learns' as the management of HF continues to evolve.
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.
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 | 0.055 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".