Prevalence of hepatic steatosis and associated factors in Iranian patients with chronic hepatitis C.
Bibliographic record
Abstract
BACKGROUND: Hepatic steatosis is commonly observed in patients with chronic hepatitis C (CHC). Many studies indicate a relationship between steatosis and fibrosis progression. The aim of this study was to analyze the prevalence of hepatic steatosis and related factors in Iranian CHC patients. METHODS: One hundred and fifteen consecutive patients with CHC were enrolled which were treatment- naïve. The patients were divided into groups with and without steatosis according to the result of liver biopsy (58.3% and 41.7%, respectively). Demographic, histological, biochemical and virological factors were examined and compared in all patients. RESULTS: In terms of host factors, body mass index (BMI), triglyceride, fasting blood glucose (FBG), necroinflammatory activity and severity in fibrosis of CHC patients with steatosis was significantly higher than the patients without steatosis. Of viral factors, HCV viral load was not significantly altered in patients with steatosis. Moreover, HCV genotypes did not meet such association. Using multivariate regression analysis, parameters of BMI values, FBG level and stage of fibrosis were independently associated with steatosis. CONCLUSION: Our data indicate that CHC patients are more susceptible to development of hepatic steatosis. Based on our results, grade of steatosis appears to be associated with hepatic fibrosis progression rate in CHC patients.
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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 | 0.001 |
| 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 | 0.000 | 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".