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Record W2293354473 · doi:10.1586/14779072.2016.1165092

Ivabradine for the treatment of chronic heart failure

2016· review· en· W2293354473 on OpenAlexaff
Christine Henri, Eileen O’Meara, Simon de Denus, Lynn Elzir, Jean‐Claude Tardif

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2016
Typereview
Languageen
FieldMedicine
TopicHeart rate and cardiovascular health
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsIvabradineMedicineHeart failureCardiologyEjection fractionHeart rateInternal medicineSinus rhythmContext (archaeology)Beta blockerAtrial fibrillationBlood pressure

Abstract

fetched live from OpenAlex

Several studies have underlined the beneficial effects of a lower heart rate on mortality in patients with chronic heart failure and reduced ejection fraction. In clinical practice, achieving a heart rate ≤70 bpm with beta-blockers is not always possible. In this context, the more recent guidelines added ivabradine to the management of those patients if heart rate remains ≥70 bpm in sinus rhythm and symptoms persist despite treatment with an evidence-based or maximum tolerated dose of a beta-blocker, an angiotensin converting enzyme inhibitor or angiotensin-receptor blocker, and a mineralocorticoid receptor antagonist. Ivabradine is a well-tolerated, safe and effective treatment option with the objective to improve prognosis, left ventricular structure and function, exercise tolerance and quality of life. Accordingly, the following article will evaluate the benefits of a combination of the currently recommended pharmacological therapy in chronic heart failure with the selective heart rate reducing agent ivabradine.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.385
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2016
Admission routes1
Has abstractyes

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