Ischemic stroke in liver cirrhosis: epidemiology, risk factors, and in-hospital outcomes
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with cirrhosis are reportedly more prone to develop hemorrhagic stroke, thereby increasing the risk of death. However, the effect of ischemic stroke on liver diseases remains unclear. In addition, few studies have explored the risk factors for ischemic stroke in patients with liver cirrhosis. Our study aimed to explore the epidemiology, risk factors, and in-hospital outcomes of ischemic stroke in a large cohort of hospitalized patients with cirrhosis. PATIENTS AND METHODS: In this single-center observational study, we retrospectively reviewed the medical records of patients with liver cirrhosis admitted to our hospital from January 2011 to June 2014. A diagnosis of ischemic stroke was further identified. RESULTS: Of the 2444 patients with liver cirrhosis, 160 had ischemic stroke, including 128 patients with previous ischemic stroke and 32 patients with new-onset ischemic stroke during their hospitalizations. Compared with patients with cirrhosis without ischemic stroke, those with ischemic stroke were significantly older; had a significantly higher proportion of arterial hypertension and a significantly lower proportion of hepatitis B virus infection; had significantly higher white blood cell, platelet, blood urea nitrogen, and triglyceride levels; and had significantly lower alanine aminotransferase and aspartate aminotransferase levels and prothrombin time. The in-hospital mortality was significantly higher in patients with ischemic stroke than in those without [8.80% (14/160) vs. 3.2% (72/2284), P=0.001]. CONCLUSION: Ischemic stroke was often observed in patients with cirrhosis, and it significantly increased the in-hospital mortality. The association of inflammation, coagulation disorders, and viral hepatitis with development of ischemic stroke in liver cirrhosis should be further evaluated in prospective cohort studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".