Response-Guided Peginterferon Therapy in Hepatitis B E Antigen-Positive Chronic Hepatitis B Using Serum Hepatitis B Surface Antigen Levels
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it