BNP‐Guided Therapy Not Better Than Expert's Clinical Assessment for β‐Blocker Titration in Patients With Heart Failure
Bibliographic record
Abstract
B-type natriuretic peptide (BNP) is a cardiac neurohormone used as a noninvasive tool for diagnosing and monitoring heart failure. Beta blockers have beneficial effects in patients with heart failure as well as a direct effect on BNP plasma levels. The aim of this study is to compare the efficacy of a BNP-guided approach vs. standard care on beta-blocker titration in heart failure patients. Forty-one patients with heart failure were randomized into a clinical trial. Bisoprolol was started, and the dose was regularly up-titrated. BNP was measured monthly. The clinical group had beta-blocker dosage increased according to standard care, whereas the BNP group had beta-blocker dosage up-titrated according to plasma BNP levels plus standard care. The primary outcome was mean beta-blocker dose achieved after 3 months. BNP levels, left ventricular ejection fraction, clinical score, quality of life, and hospitalization were collected in all patients. BNP-guided up-titration of beta blocker in ambulatory patients with heart failure did not result in higher doses of beta blocker at the end of 3 months+/-SD (5.9+/-4.3 mg vs. 4.4+/-3.4 mg, p=0.22). Left ventricular ejection fraction was significantly improved in both groups by 7.3% (95% confidence interval, 4.1%-10.4%; p<0.0001). A trend toward better quality of life was seen in the BNP group.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".