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BNP‐Guided Therapy Not Better Than Expert's Clinical Assessment for β‐Blocker Titration in Patients With Heart Failure

2005· article· en· W2085213864 on OpenAlexaff
Margaret Fraser, Kathryn Williams, Haissam Haddad

Bibliographic record

VenueCongestive Heart Failure · 2005
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineHeart failureEjection fractionBisoprololInternal medicineBeta blockerCardiologyAmbulatoryConfidence intervalRandomized controlled trialNatriuretic peptideBrain natriuretic peptide

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.347
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2005
Admission routes1
Has abstractyes

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