Bibliographic record
Abstract
PURPOSE OF REVIEW: This review focuses on recent literature pertaining to the role of B-type natriuretic peptide (BNP) in heart failure. RECENT FINDINGS: Heart failure is a common disorder that is associated with significant mortality and morbidity. The diagnosis of heart failure may at times be difficult when using conventional tools. The cardiac natriuretic peptides, particularly BNP, have evolved to be useful biomarkers in heart failure and other cardiovascular disorders. Recent studies have established a close association between plasma BNP and the amino-terminal fragment of the BNP prohormone (NT-proBNP) with the diagnosis of heart failure and independent prediction of mortality and heart failure events. Furthermore, preliminary data from randomized controlled trials suggest that knowledge of BNP and/or NT-proBNP level may optimize the management of patients with heart failure. Exogenous natriuretic peptide in the form of recombinant human BNP (nesiritide) has been shown to improve hemodynamics and dyspnea and is approved in the USA and several other countries for the management of patients with acute decompensated heart failure. The effect of nesiritide on clinical outcome, however, remains unclear. SUMMARY: When used in the appropriate clinical settings, BNP or NT-proBNP testing is extremely useful in establishing diagnosis and predicting prognosis in heart failure. Nesiritide holds promise in the management of patients with acute decompensated heart failure. Large-scale randomized controlled trials to evaluate BNP/NT-proBNP-guided therapy are currently in progress and studies of the impact of exogenous BNP on clinical outcomes in heart failure are likely to be forthcoming.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".