Management of dyslipidaemia in patients with comorbidities—facing the challenge
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".