Global Innovations in the Care of Patients With Heart Failure
Bibliographic record
Abstract
The prevalence of heart failure (HF) is increasing in many regions of the world, particularly within the context of aging populations in many countries. The Heart Failure Society of America (HFSA) sought to explore areas of global HF innovation with the goal of exchanging ideas and best practices internationally. The HFSA Annual Scientific Meeting included roundtable discussions focused on the challenges faced by each of the participating regions and sharing innovative solutions. Themes identified include the lack of high-quality region-specific HF registry data that is required to accurately define patient needs and to facilitate outcome metrics; the tension between providing care that is accessible to the patient vs. concentrating highly-specialized care within tertiary centers; the need to accredit and coordinate HF care across a spectrum of healthcare delivery centers within regions; opportunities to improve the prevention and timely diagnosis of HF to enhance population outcomes, especially in communities facing healthcare disparities; and the evolution of multidisciplinary team-based care, particularly in optimizing access to guideline-directed medical therapies. This article summarizes the major themes that emerged during the roundtable sessions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".