Risk of Stroke in Liver Cirrhosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Liver cirrhosis can lead to abnormal coagulation, rendering patients at risk for bleeding but also thrombotic complications. We conducted a systematic review and meta-analysis to explore the epidemiology of stroke in liver cirrhosis and the potential association between them. MATERIALS AND METHODS: Studies were searched through the PubMed, EMBASE, and Cochrane Library databases. Incidence and prevalence of unspecific stroke, hemorrhagic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and ischemic stroke were pooled by using a random-effect model. Meta-regression analyses were employed to explore the sources of heterogeneity. As for the cohort studies, hazard ratios (HRs) with 95% CIs were pooled to evaluate the association between liver cirrhosis and stroke. RESULTS: Twenty-seven studies with 93,191 cirrhotic patients were included, of which 23 explored the incidence and 10 explored the prevalence. The pooled incidence of unspecific stroke, hemorrhagic stroke, intracranial hemorrhage, and ischemic stroke was 4.1%, 1.3%, 2.0%, and 3.7%, respectively. The pooled prevalence of unspecific and ischemic stroke was 9.0% and 2.6%, respectively. Heterogeneity among studies was significant in most of meta-analyses. Meta-regression analyses indicated that the sample size might explain the potential source of heterogeneity (P=0.018). Liver cirrhosis significantly increased the risk of subarachnoid (HR=2.36; 95% CI, 1.80-3.09; P=0.000) and intracranial hemorrhage (HR=1.48; 95% CI, 1.06-2.05; P=0.020), but not unspecific (HR=1.02; 95% CI, 0.49-2.14; P=0.960), ischemic (HR=0.79; 95% CI, 0.46-1.35; P=0.380), or hemorrhagic stroke (HR=1.88; 95% CI, 0.52-6.81; P=0.335). CONCLUSIONS: Stroke is uncommon in cirrhotic patients. However, considering a positive relationship of liver cirrhosis with subarachnoid and intracranial hemorrhage, the prophylactic strategy may be selectively adopted in cirrhotic patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".