Gastric antral vascular ectasia is more frequent in patients with non-alcoholic steatohepatitis-induced cirrhosis
Bibliographic record
Abstract
Background: Gastric antral vascular ectasia (GAVE) is an uncommon cause of occult gastrointestinal (GI) bleeding. Based on clinical observations, we hypothesized that GAVE was more common in patients with non-alcoholic steatohepatitis (NASH) cirrhosis. Methods: We performed this retrospective study at Centre Hospitalier de l’Université de Montréal (CHUM). We included all cirrhotic patients who had undergone an esophagogastroduodenoscopy (EGD) between 2009 and 2011. GAVE was diagnosed based on a typical endoscopic appearance. NASH cirrhosis was diagnosed in patients with a metabolic syndrome after excluding other causes of liver disease. GAVE was considered symptomatic if it required treatment. Results: We included 855 cirrhotic patients in the study. The median age was 58 (range 19–88) years. The etiology of cirrhosis was as follows: NASH in 18% ( n = 154), autoimmune diseases in 15.1% ( n = 129), hepatitis B virus (HBV) in 6.3% ( n = 54), hepatitis C virus (HCV) in 19.4% ( n = 166), alcohol in 25.7% ( n = 220), alcohol plus HCV in 7.8% ( n = 67), cryptogenic in 2.8% ( n = 24), and other etiologies in 4.8% ( n = 41). GAVE was more frequently observed among patients with NASH cirrhosis than in cirrhosis of other etiologies (29.2% vs. 9.4%, respectively; p < 0.001). In multivariate analysis, NASH was strongly associated with GAVE with an odds ratio (OR) of 3.73 (95% CI 2.36 to 5.90, p < 0.001), and the association was stronger with symptomatic GAVE (OR 5.77, 95% CI 2.93 to 11.38). Conclusions: NASH cirrhosis is a major risk factor for GAVE and symptomatic GAVE.
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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 | 0.001 |
| 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) | 0.003 | 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".