Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent literature on the role of biomarkers in heart failure is reviewed, focusing on B-type natriuretic peptide. RECENT FINDINGS: Knowledge of the processes which increase ventricular stress, thus increasing B-type natriuretic peptide, is key to appropriate utilization and interpretation of B-type natriuretic peptide levels. B-type natriuretic peptide is a useful adjunct to confirm or rule out heart failure. B-type natriuretic peptide is a robust prognostic indicator in all stages of heart failure, with prognostic significance in patients undergoing cardiac and noncardiac surgery, and in those with acute coronary syndromes. Serial B-type natriuretic peptide testing predicts outcomes in hospitalized patients with heart failure. The role of B-type natriuretic peptide in screening high-risk populations is promising, but its use in unselected populations is unclear. There is increasing evidence that the use of B-type natriuretic peptide to guide heart failure management is associated with improved clinical outcomes and reduced health costs. SUMMARY: Biomarkers play an important role in heart failure, but there remain unanswered questions regarding optimization of their use. They should be used as an adjunct to, not replacement for, clinical assessment. Currently available B-type natriuretic peptide assays have limitations relating to clinical variability and assay specificity. Other neurohormonal, inflammatory and metabolic markers may add complementary information to that provided by currently available B-type natriuretic peptide assays.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".