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Record W2945710519 · doi:10.1161/circresaha.118.313650

Medical Therapy for Heart Failure Associated With Pulmonary Hypertension

2019· review· en· W2945710519 on OpenAlexafffund
Jason G.E. Zelt, Ketul R. Chaudhary, Virgilio J. J. Cadete, Lisa Mielniczuk, Duncan J. Stewart

Bibliographic record

VenueCirculation Research · 2019
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsOttawa HospitalSt. Michael's HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsHeart failurePulmonary hypertensionMedicineCardiologyIntensive care medicineMedical therapyInternal medicine

Abstract

fetched live from OpenAlex

The past 2 decades have witnessed a >40% improvement in mortality for patients with heart failure and left ventricular systolic dysfunction. 1 This success has coincided with the stepwise availability of drugs that target neurohormonal activation: β-adrenergic receptor blockers (β-blockers), ACE (angiotensin-converting enzyme) inhibitors and ANG (angiotensin) II blockers, neprilysin inhibitors, and aldosterone antagonists. Our understanding of right heart failure (RHF) has lagged behind and many proven targeted therapies for left heart failure do not appear to provide similar benefits for RHF. Until recently, the right ventricle (RV) has often been viewed as less important than the left ventricle and in contemporary literature received the moniker “The Forgotten Ventricle”. Recent advances in echocardiography and magnetic resonance imaging have enabled detailed assessments of RV anatomy and physiology in both health and disease allowing us to more accurately describe the clinical sequelae and end-organ manifestations of RHF. RV function is now recognized as one of the most important predictors of prognosis in many cardiovascular disease states. 2 Despite the significance of RV function to survival, there are no clinically approved therapies that directly nor selectively improve RV function. As well, relative to our understanding of left heart failure, the basis for RHF remains poorly understood. This article aims to condense the current knowledge on RV adaptation and failure, review current management strategies for RHF, and explore evolving therapeutic approaches.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.234
GPT teacher head0.446
Teacher spread0.213 · 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 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

Citations69
Published2019
Admission routes2
Has abstractyes

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