Response-Guided Peginterferon Therapy in Hepatitis B E Antigen-Positive Chronic Hepatitis B Using Serum Hepatitis B Surface Antigen Levels
Bibliographic record
Abstract
UNLABELLED: On-treatment levels of hepatitis B surface antigen (HBsAg) may predict response to peginterferon (PEG-IFN) therapy in chronic hepatitis B (CHB), but previously proposed prediction rules have shown limited external validity. We analyzed 803 HBeAg-positive patients treated with PEG-IFN in three global studies with available HBsAg measurements. A stopping-rule based on absence of a decline from baseline was compared to a prediction-rule that uses HBsAg levels of <1,500 IU/mL and >20,000 IU/mL to identify patients with high and low probabilities of response. Patients with an HBsAg level <1,500 IU/mL at week 12 achieved response (HBeAg loss with HBV DNA <2,000 IU/mL at 6 months posttreatment) in 45%. At week 12, patients without a decline in HBsAg achieved a response in 14%, compared to only 6% of patients with HBsAg >20,000 IU/mL, but performance varied across HBV genotype. In patients treated with PEG-IFN monotherapy (n = 465), response rates were low in patients with genotypes A or D if there was no decline of HBsAg by week 12 (negative predictive value [NPV]: 97%-100%), and in patients with genotypes B or C if HBsAg at week 12 was >20,000 IU/mL (NPV: 92%-98%). At week 24, nearly all patients with HBsAg >20,000 IU/mL failed to achieve a response, irrespective of HBV genotype (NPV for response and HBsAg loss 99% and 100%). CONCLUSION: HBsAg is a strong predictor of response to PEG-IFN in HBeAg-positive CHB. HBV genotype-specific stopping-rules may be considered at week 12, but treatment discontinuation is indicated in all patients with HBsAg >20,000 IU/mL at week 24, irrespective of HBV genotype.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".