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Record W4405796144 · doi:10.1093/ehjcvp/pvae095

Management of dyslipidaemia in patients with comorbidities—facing the challenge

2024· review· en· W4405796144 on OpenAlexaff
Lisa Frühwald, Peter Fasching, Dobromir Dobrev, Juan Carlos Kaski, Claudio Borghi, Sven Waßmann, Kurt Huber, Anne Grete Semb, Stefan Agewall, Heinz Drexel

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Pharmacotherapy · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsDiscontinuationMedicineDiseaseIntensive care medicineLiver diseasePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

This review aims to examine the evidence on the benefits and risks of lipid-lowering drugs in patients with liver disease. Elevated liver enzyme levels often lead to cautious discontinuation of these drugs, potentially withholding from patients their benefit in reducing cardiovascular disease morbidity and mortality. Using a literature search of PubMed, we examine the efficacy and safety profiles of various lipid-lowering agents, including statins, ezetimibe, bempedoic acid, PCSK9 inhibitors, fibrates, and icosapent ethyl, focusing particularly on their potential side effects related to liver health. A major challenge in the assessment of drug-induced hepatotoxicity is the fact that it relies heavily on case reports rather than real-world evidence. There is currently a lack of robust evidence on lipid-lowering therapy in people with pre-existing liver disease. Nevertheless, we have attempted to summarize the available data for all the drugs mentioned in order to provide guidance for the treatment of patients with liver dysfunction. This review highlights the need for further research to optimize treatment strategies for patients with coexisting liver and cardiovascular disease.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.342
Teacher spread0.284 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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