The Role of Natriuretic Peptides in Pericardial Fluid in Predicting Cardiovascular Disorders: A Systematic Review
Bibliographic record
Abstract
Atrial and ventricular natriuretic peptides play an important role in the neurohormonal regulation of cardiac function. Plasma levels of these peptides may aid in the diagnosis and prognosis of different cardiac disorders, such as congestive heart failure, ischemic heart disease, and atrial fibrillation. However, the association between elevated pericardial fluid levels of natriuretic peptides and these clinical conditions has not been proven. Databases Medline, EMBASE, Cochrane, ClinicalTrials.gov, and Google Scholar were searched for primary studies evaluating atrial natriuretic peptide, B-type natriuretic peptide, and N-terminal-pro-B-type natriuretic peptide concentrations within the pericardial fluid in various cardiac disorders. A total of 1060 citations were screened, of which 38 studies underwent a full-text evaluation, and 10 were finally included in this review. Sample size varied across studies (n = 8-148), and there was a total of 577 patients across the 10 studies. Findings suggested that pericardial fluid levels of B-type natriuretic peptide and N-terminal-pro-B-type natriuretic peptide but not atrial natriuretic peptide, may correlated with the reported cardiac conditions. Our findings suggest that pericardial fluid levels of natriuretic peptides may correlate with some cardiac disorders such as congestive heart failure and atrial fibrillation. The addition of these peptides to the existing clinical risk stratification scores may be helpful in the early diagnosis and management of these conditions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".