Hepatitis B core‐related antigen monitoring during peginterferon alfa treatment for HBeAg‐negative chronic hepatitis B
Bibliographic record
Abstract
Serum Hepatitis B core-related antigen (HBcrAg) level moderately correlates with cccDNA. We examined whether HBcrAg can add value in monitoring the effect of peginterferon (PEG-IFN) therapy for HBeAg-negative chronic hepatitis B (CHB) infection. Thus, serum HBcrAg level was measured in 133 HBeAg-negative, mainly Caucasian CHB patients, treated with 48 weeks of PEG-IFN alfa-2a. We assessed its association with response (ALT normalization & HBV DNA < 2000 IU/mL) at week 72. HBcrAg level strongly correlated with HBV DNA level (r = 0.8, P < 0.001) and weakly with qHBsAg and ALT (both r = 0.2, P = 0.01). At week 48, mean HBcrAg decline was -3.3 log U/mL. Baseline levels were comparable for patients with and without response at week 72 (5.0 vs 4.9 log U/mL, P = 0.59). HBcrAg decline at week 72 differed between patients with and without response (-2.4 vs -1.0 log U/mL, P = 0.001), but no cut-off could be determined. The pattern of decline in responders resembled that of HBV DNA, but HBcrAg decline was weaker (HBcrAg -2.5 log U/mL; HBV DNA: -4.0 log IU/mL, P < 0.001). For early identification of nonresponse, diagnostic accuracy of HBV DNA and qHBsAg decline at week 12 (AUC 0.742, CI-95% [0.0.629-0.855], P < 0.001) did not improve by adding HBcrAg decline (AUC 0.747, CI-95% [0.629-0.855] P < 0.001), nor by replacing HBV DNA decline by HBcrAg decline (AUC 0.754, CI-95% [0.641-0.867], P < 0.001). In conclusion, in Caucasian patients with HBeAg-negative CHB, decline of HBcrAg during PEG-IFN treatment was stronger in patients with treatment response. However, HBcrAg was not superior to HBV DNA and qHBsAg in predicting response during PEG-IFN treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".