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Record W2596885499 · doi:10.1042/cs20160196

Advances in heart failure: a review of biomarkers, emerging pharmacological therapies, durable mechanical support and telemonitoring

2017· review· en· W2596885499 on OpenAlexaff
Yasbanoo Moayedi, Heather J. Ross

Bibliographic record

VenueClinical Science · 2017
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsCopeptinHeart failureMedicineContext (archaeology)Intensive care medicineClinical trialBioinformaticsCardiologyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this review is to provide an overview of diagnosis, prognosis and management of heart failure (HF) with reduced ejection fraction (HFrEF). Specifically, this review is divided into three sections. The first section will address biomarkers. The discovery of biomarkers has allowed further understanding of the pathophysiology of HF and provides insight into potential therapeutic targets. This review will focus on novel applications of natriuretic peptides (NPs) in clinical trials. Next, emerging biomarkers of HF, such as ST2, galectin-3 and copeptin, will be discussed. The second section aims to highlight HF therapies, including novel drugs and durable devices. The last section will review home haemodynamic monitoring and mobile health. We aim to provide context for the understanding of novel diagnostic and therapeutic advances in HF that are still in phase II or III trials, and have yet to become widely available.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.152
GPT teacher head0.504
Teacher spread0.352 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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