The Heart Failure Association Atlas: Rationale, Objectives, and Methods
Bibliographic record
Abstract
Heart failure (HF) constitutes the growing cardiovascular burden and the major public health issue, but comprehensive statistics on HF epidemiology and related management in Europe are missing. The Heart Failure Association (HFA) Atlas has been initiated in 2016 in order to close this gap, representing the continuity directly rooted in the European Society of Cardiology (ESC) Atlas of Cardiology. The major aim of the HFA Atlas is to establish a contemporary dataset on HF epidemiology, resources and reimbursement policies for HF management, organization of the National Heart Failure Societies (NHFS) and their major activities, including education and HF awareness. These data are gathered in collaboration with the network of NHFS of the ESC member and ESC affiliated countries. The dataset will be continuously improved and advanced based on the experience and enhanced understanding of data collection in the forthcoming years. This will enable revealing trends, disparities and gaps in knowledge on epidemiology and management of HF. Such data are highly needed by the clinicians of different specialties (aside from cardiologists and cardiac surgeons), researchers, healthcare policy makers, as well as HF patients and their caregivers. It will also allow to map the snapshot of realities in HF care, as well as to provide insights for evidence-based health care policy in contemporary management of HF. Such data will support the ESC/HFA efforts to improve HF management ant outcomes through stronger recommendations and calls for action. This will likely influence the allocation of funds for the prevention, treatment, education and research in HF.
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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 | 0.068 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".