Sonography Predicts Liver Steatosis in Patients With Chronic Hepatitis B
Bibliographic record
Abstract
OBJECTIVES: Liver inflammation and fibrosis may impair the ability of sonography to identify steatosis. We determined the accuracy of sonography in grading steatosis in patients with chronic hepatitis B compared to liver biopsy. METHODS: We conducted was a single-center retrospective study of all nontransplanted patients with chronic hepatitis B undergoing sonography and liver biopsy between 2004 and 2014 (n = 109). Steatosis was graded by sonography as none, mild, moderate, or severe. Liver histologic analysis graded steatosis (0, <5%; 1, <33%; 2, <66%; or 3, ≥66%) and staged fibrosis (F0-F4). Severe steatosis was defined as grade 2 or 3. Clinical variables within 6 months of liver biopsy were collected, and the association with steatosis was analyzed by univariate logistic regression. RESULTS: Patients were predominantly Asian (83%), male (62%), and hepatitis B e antigen negative (62%). Twenty-nine percent of patients were obese; 9% had diabetes mellitus; 23% had hypertension; and 31% had dyslipidemia. Forty-four percent of patients had steatosis on liver biopsy; 8% had severe steatosis. The presence of any steatosis on sonography correctly identified any steatosis on liver biopsy in 29 of 48 patients (60%). The absence of steatosis on sonography ruled out severe steatosis on biopsy (specificity, 100%). Severe steatosis on sonography correctly predicted the presence of severe steatosis on liver biopsy (89%; P < .001); however, it was not accurate at distinguishing between steatosis grades. Predictors of biopsy-proven steatosis on univariate analysis included diabetes (P < .001), hypertension (P = .03), hypercholesterolemia (P = .02), and body mass index (P < .001). CONCLUSIONS: Sonography had excellent accuracy in identifying patients with steatosis on biopsy. Abdominal sonography can be used to predict clinically important steatosis in patients with chronic hepatitis B.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".