Long-Term Follow-up and Quantitative Hepatitis B Surface Antigen Monitoring in North American Chronic HBV Carriers
Bibliographic record
Abstract
Introduction. Quantitative hepatitis B surface antigen (qHBsAg) combined with HBV DNA may be useful for predicting chronic hepatitis B (CHB) activity and nucleoside analogue (NA) response. Material and methods. In this retrospective cohort study we evaluated qHBsAg levels according to CHB disease phase and among patients on treatment. Random effect logistic regression analysis was used to analyze qHBsAg change with time in the NA-treated cohort. Results. 545 CHB carriers [56% M, median age 48 y (IQR 38-59), 73% Asian] had qHBsAg testing. In the untreated group (44%), 8% were classified as immune tolerant, 10% immune clearance, 40% inactive, and 43% had HBeAg- CHB and the median HBsAg levels were 4.6 (IQR 3.4-4.9), 4.0 (IQR 3.4-4.5), 2.9 (IQR 1.4-3.8), and 3.2 log IU/mL (IQR 2.6-4.0), respectively; p < 0.001. In the NA-treated group (28% entecavir, 68% tenofovir, 4% lamivudine), no significant change in qHBsAg levels occured with time. However, 19% of patients on long-term NA had sustained qHBsAg < 2 log10 IU/mL. Conclusion. qHBsAg titers were associated with CHB phase and remained stable in those on long-term NA. A significant number of treated patients had low-level qHBsAg, of which some may be eligible for treatment discontinuation without risk of flare.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".