Natriuretic peptides in unstable coronary artery disease
Bibliographic record
Abstract
Patients with unstable coronary artery disease (CAD), i.e., unstable angina or non-ST-elevation myocardial infarction, vary widely in clinical presentation, prognosis and response to treatment. To select appropriate therapy, early risk stratification has become increasingly important. This review focuses on the emerging role of natriuretic peptides in the early assessment of patients with unstable CAD. We conclude that levels of brain natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (NT-proBNP) are strongly associated to mortality and the risk of future congestive heart failure, and carry important prognostic information independent from previously known risk factors in unstable CAD. There are some data indicating that these markers can also be helpful in the selection of appropriate therapy in these patients but further studies are needed. Before a routine use of BNP or NT-proBNP in unstable CAD can be recommended, the cost-effectiveness of adding these new markers to the currently routine markers and their impact on selection of treatment needs further evaluation.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 0.003 |
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".