Natriuretic Peptides: Role in the Diagnosis and Management of Heart Failure: A Scientific Statement from the Heart Failure Association of the European Society of Cardiology, Heart Failure Society of America and Japanese Heart Failure Society
Bibliographic record
Abstract
Natriuretic peptides, brain (B-type) natriuretic peptide (BNP) and N-terminal prohormone of brain natriuretic peptide (NT-proBNP) are globally and most often used for the diagnosis of heart failure (HF). In addition, they can have an important complementary role in the risk stratification of its prognosis. Since the development of angiotensin receptor-neprilysin inhibitors (ARNIs), the use of natriuretic peptides as therapeutic agents has grown in importance. The present document is the result of the Trilateral Cooperation Project among the Heart Failure Association of the European Society of Cardiology, the Heart Failure Society of America and the Japanese Heart Failure Society. It represents an expert consensus that aims to provide a comprehensive, up-to-date perspective on natriuretic peptides in the diagnosis and management of HF, with a focus on the following main issues: (1) history and basic research: discovery, production and cardiovascular protection; (2) diagnostic and prognostic biomarkers: acute HF, chronic HF, inclusion/endpoint in clinical trials, and natriuretic peptide-guided therapy; (3) therapeutic use: nesiritide (BNP), carperitide (ANP) and ARNIs; and (4) gaps in knowledge and future directions.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 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".