Optimizing Outcomes in Heart Failure: 2022 and Beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it