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 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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".