Use of Lipid-Lowering Drugs After Intracerebral Hemorrhage
Bibliographic record
Abstract
Hyperlipidemia is common in patients with intracerebral hemorrhage (ICH). Accumulating evidence indicates that patients with ICH are at risk for future hemorrhage recurrence, cardiovascular disease, and ischemic stroke and highlights the importance of secondary prevention of vascular events after ICH. Although the benefits of intensive treatment of hyperlipidemia for reducing ischemic cardiac and vascular events in patients with ischemic stroke are well established, the benefit versus harm in patients with ICH are less clear. Epidemiological studies suggest that hyperlipidemia is protective against ICH and that intensive lowering of lipids is associated with increased risk for ICH. Similarly, although currently available lipid-lowering treatments have been thoroughly studied in patients with ischemic cardiac and vascular disease, only few randomized trials of these therapies included a very small number of patients with history of ICH. Thus, limiting any definitive conclusions regarding the safety and net benefit of these treatments in ICH populations. Currently, there is no consensus regarding the optimal strategy for management of hyperlipidemia after ICH. In this article, we review relevant literature to outline the competing risks and benefits of lipid-lowering treatments in this vulnerable patient population. We suggest a treatment paradigm based on available data but note that data from dedicated randomized trials are needed to build the necessary evidence to guide optimal lipid-lowering strategy in patients with a history of ICH.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".