Abstract TP189: Statin Therapy and Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis of Mortality Risk
Bibliographic record
Abstract
Objective: To determine the association between intracerebral hemorrhage mortality and statin therapy, aiming to provide effective methods to prevent intracerebral hemorrhage and reduce the mortality rate. Background: Intracerebral hemorrhage (ICH) poses significant challenges due to its high mortality and disability rates, especially among Asian populations. Contributing factors such as hypocholesterolemia and hypertension amplify the risk of ICH and subsequent hematoma expansion, underscoring the urgent need for effective interventions. Despite the lack of established pharmacological treatments for ICH, statins have emerged as promising candidates for neuroprotection, attributed to their pleiotropic effects beyond lipid-lowering properties. Methods: This study meticulously conducted a systematic search of various databases until February 2024 to identify relevant literature on statin therapy following ICH. The inclusion criteria encompassed randomized controlled trials (RCTs) and observational cohort studies, ensuring a comprehensive analysis of the available evidence. Data extraction was performed rigorously, involving screening, extraction, and cross-checking by two independent investigators utilizing a predefined table. Quality assessment was carried out using the Newcastle-Ottawa Scale, a recognized tool for evaluating observational studies. Results: The analysis of 14 studies comprising 86,838 patients revealed a substantial reduction in mortality associated with statin therapy post-ICH, with an odds ratio (OR) of 0.37 and a 95% confidence interval (CI) of 0.25-0.57 (p < 0.00001). However, heterogeneity was observed in studies assessing outcomes such as intraventricular hemorrhage and Glasgow Coma Scale (GCS), indicating variability in populations or methodologies. Conclusion: One of the significant life-threatening condicions is ICH, and patients are associated with poor prognosis. There is no effective pharmacological treatment to decrease ICH mortality, While statin therapy demonstrates promising potential in mitigating post-ICH mortality, this study underscores the necessity for further research to elucidate optimal treatment duration and address existing heterogeneity. Standardized studies are imperative to inform evidence-based clinical decisions and improve outcomes for individuals afflicted with intracranial hemorrhage.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".