Natriuretic Peptides in Heart Failure
Bibliographic record
Abstract
Heart failure is a major global health problem affecting 23 million people worldwide. As more cardiac patients survive and live longer with this progressive disease, heart failure is a condition for which the prevalence will grow. Based solely on clinical presentation, heart failure can be difficult to diagnose since its presentation is complex, with signs and symptoms that are nonspecific and may not always be present. B-type natriuretic peptide (BNP)7 and N-terminal proBNP (NT-proBNP) are well established, clinically validated biomarkers that have been shown to improve the diagnostic accuracy for heart failure and provide prognostic information for risk stratification. The widespread clinical use of these biomarkers for more than a decade is reflected by their incorporation into national and international medical guidelines for heart failure, at the highest classification for recommendation. BNP is a cardiac hormone secreted by cardiomyocytes into the circulation in response to states of volume expansion and pressure overload, as is the case in heart failure. BNP's diuretic, natriuretic, and vasodilatory actions, and its protective effects on endothelial function and vascular remodeling, act to relieve the adverse consequences of heart failure. During the synthesis and processing of BNP, its 108 amino acid biologically inactive precursor, proBNP, is proteolytically cleaved to form the 32 amino acid peptide BNP and the 76 amino acid peptide NT-proBNP. While BNP is physiologically active, NT-proBNP is biologically inert. Due to its secretion at a 1:1 ratio to BNP and its longer half-life (90–120 min vs 20 min for BNP), the measurement of NT-proBNP has proven to have an essentially equivalent clinical performance to BNP as a biomarker for heart failure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".