State of the Art: Using Natriuretic Peptide Levels in Clinical Practice
Bibliographic record
Abstract
Natriuretic peptide (NP) levels (B-type natriuretic peptide (BNP) and N-terminal proBNP) are now widely used in clinical practice and cardiovascular research throughout the world and have been incorporated into most national and international cardiovascular guidelines for heart failure. The role of NP levels in state-of-the-art clinical practice is evolving rapidly. This paper reviews and highlights ten key messages to clinicians: 1) NP levels are quantitative plasma biomarkers of heart failure (HF). 2) NP levels are accurate in the diagnosis of HF. 3) NP levels may help risk stratify emergency department (ED) patients with regard to the need for hospital admission or direct ED discharge. 4) NP levels help improve patient management and reduce total treatment costs in patients with acute dyspnoea. 5) NP levels at the time of admission are powerful predictors of outcome in predicting death and re-hospitalisation in HF patients. 6) NP levels at discharge aid in risk stratification of the HF patient. 7) NP-guided therapy may improve morbidity and/or mortality in chronic HF. 8) The combination of NP levels together with symptoms, signs and weight gain assists in the assessment of clinical decompensation in HF. 9) NP levels can accelerate accurate diagnosis of heart failure presenting in primary care. 10) NP levels may be helpful to screen for asymptomatic left ventricular dysfunction in high-risk patients.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".