Hepatitis B virus quantitative surface antigen levels differ by genotype
Bibliographic record
Abstract
Background: Hepatitis B virus (HBV) antiviral treatment is guided by HBV DNA levels, liver enzyme values, and fibrosis score. Quantitative hepatitis B surface antigen (qHBsAg) may represent a more cost-effective and less labour-intensive surrogate for HBV DNA. The influence of HBV genotype on qHBsAg has not been well considered. We explored the relationship between HBV DNA and qHBsAg as well as the influence of genotype. Methods: Genotype, HBV DNA, and qHBsAg levels for 138 non-HBV antiviral-treated patients followed at The Ottawa Hospital Viral Hepatitis Program were assessed. Correlations between HBV DNA and qHBsAg as a function of HBV genotype were evaluated. Results: Mean age was 44.5 years; 52.2% were male, 52.3% Asian, and 34.9% Black. Overall median HBV DNA was 2,557 IU/mL. Highest median HBV DNA was in genotypes B (7,899 IU/mL) and C (39,900 IU/mL) and the lowest in genotype E (684 IU/mL). Median qHBsAg overall was 2,000 IU/mL. Highest median qHBsAg was in genotype E (9665 IU/mL) and lowest in genotypes B (300 IU/mL) and C (1,913 IU/mL). HBV DNA-to-qHBsAg ratio differed in direction and magnitude by genotype. HBV DNA and qHBsAg were positively correlated for genotypes A, B, and D but not correlated for genotypes C and E. Age, HBeAg status, and genotype independently predicted HBsAg level and log10 HBV DNA-to-log10 qHBsAg ratio by multi-variable median regression analysis. Conclusions: Median amounts and correlations between HBV DNA and qHBsAg differ in magnitude and direction depending on genotype. This knowledge may be relevant to HBV antiviral treatment guideline development.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".