Hepatitis B Surface Antigen Levels Can Be Used to Rule Out Cirrhosis in Hepatitis B e Antigen-Positive Chronic Hepatitis B: Results From the SONIC-B Study
Bibliographic record
Abstract
BACKGROUND: Serum hepatitis B surface antigen (HBsAg) levels correlate with the duration of chronic hepatitis B virus (HBV) infection and may predict the extent of hepatic fibrosis. METHODS: We analyzed data from the SONIC-B database, which contains data from 8 global randomized trials and 2 large hepatology centers. Relationship between HBsAg levels and presence of significant fibrosis (Ishak 3-4) or cirrhosis (Ishak 5-6) were explored, and clinically relevant cutoffs were identified to rule out cirrhosis. RESULTS: The dataset included 2779 patients: 1866 hepatitis B e antigen (HBeAg)-positive; 322 with cirrhosis. Among HBeAg-positive patients, lower HBsAg levels were associated with higher rates of significant fibrosis (odds ratio [OR], 0.419; P < .001) and cirrhosis (OR, 0.435; P < .001). No relationship was observed among HBeAg-negative patients. Among HBeAg-positive patients, genotype-specific HBsAg cutoffs had excellent negative predictive values (>97%) and low misclassification rates (≤7.1%) and may therefore have utility in ruling out cirrhosis. Diagnostic performance of the HBsAg cutoffs was comparable among patients in whom cirrhosis could not be ruled out with fibrosis 4 (FIB-4). CONCLUSIONS: Hepatitis B virus genotype-specific HBsAg cutoffs may have utility in ruling out presence of cirrhosis in HBeAg-positive patients with genotypes B, C, and D and can be an adjunct to FIB-4 to reduce the need for further testing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".