Bibliographic record
Abstract
Brain natriuretic peptide (BNP) plays an important role in cardiovascular homeostasis. Plasma BNP increases markedly in left ventricular dysfunction from several causes, and its levels in heart failure (HF) correlate with symptoms severity. BNP has recently emerged as a potentially important clinical marker for the diagnosis of HF in patients with unexplained dyspnea. Other clinical applications of BNP, such as screening for asymptomatic ventricular dysfunction, establishing the prognosis or guiding the titration of drug therapy, are under investigation and have not yet been sufficiently validated for widespread clinical use. Laboratory-based and point-of-care analyses are available for BNP and N-terminal proBNP as fully-automated immunoassays. Several patented inventions and reagents for the diagnosis of various heart pathologies provide helpful information, particularly in conjunction with other clinical tests. They also have prognostic value for future cardiovascular events. Patents owned by Scios Inc. recommended recombinant BNP for managing acute decompensated HF. However, this treatment apparently has safety problems and no proven clinical advantage over existing treatments in terms of improved survival and prevention of subsequent hospitalizations.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.019 |
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".