<scp>HB</scp>eAg levels at week 24 predict response to 8 years of tenofovir in <scp>HB</scp>eAg‐positive chronic hepatitis B patients
Bibliographic record
Abstract
Summary Background Hepatitis B e antigen (HBeAg) seroconversion is a treatment endpoint for HBeAg‐positive CHB, and a necessary precursor to HBsAg loss. Biomarkers that predict serological outcomes would be useful. Aim To evaluate the utility of measuring HBeAg levels for predicting HBeAg seroconversion and HBsAg loss under long‐term tenofovir (TDF) therapy. Methods A total of 266 patients were enrolled into a phase III study of TDF vs adefovir (ADV) for 48 weeks in HBeAg‐positive patients, followed by open‐label TDF up to 384 weeks. Serum HBeAg levels were measured for subjects with samples available at both baseline and week 24 of treatment (n = 200). Analysis compared subjects who achieved HBeAg seroconversion by week 384 vs no HBeAg seroconversion. Results HBeAg seroconversion rate was 52% by week 384. Time to HBeAg seroconversion was 80 weeks (IQR: 36‐162). HBeAg decline at week 24 was associated with HBeAg seroconversion (1.63 vs 0.90 log10 PEIU/mL, P = .002). The optimal threshold for identifying HBeAg seroconversion was HBeAg decline ≥2.2 log10 PEIU/mL at week 24, with HBeAg seroconversion achieved by 76% of patients, compared to 44% if HBeAg decline <2.2 log10 (P < .0001). HBeAg decline ≥2.2 log10 PEIU/mL at week 24 was associated with HBsAg loss in genotype A or D patients (38% vs 15%, P = .03). Precore/basal core promotor variants were associated with lower baseline HBeAg levels, but not HBeAg seroconversion. Conclusion Decline in HBeAg levels by week 24 was associated with HBeAg seroconversion and HBsAg loss in HBeAg‐positive chronic hepatitis B patients treated with long‐term TDF.
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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) | 0.000 | 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) | 0.001 | 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".