<scp>ALT</scp> flares during nucleotide analogue therapy are associated with <scp>HB</scp>sAg loss in genotype A <scp>HB</scp>eAg‐positive chronic hepatitis B
Bibliographic record
Abstract
Abstract Background Alanine aminotransferase (ALT) flares during NA therapy are uncommon but occur. Evaluation of ALT flares during nucleos(t)ide analogue (NA) therapy is important as new immunomodulatory therapies for hepatitis B virus (HBV) are developed. We evaluated the association between ALT flares and HBsAg loss during long‐term therapy for genotype A CHB. Methods This analysis included genotype A subjects from a phase III study of tenofovir vs adefovir in HBeAg‐positive HBV. ALT flare was defined as (i) a rise in ALT >2x ULN from normal ALT levels; or (ii) a rise in ALT >2x baseline ALT level. HBsAg response at week 384 was recorded as one of HBsAg loss vs HBsAg decline (≥1 log10 IU/mL decline) vs non‐response. The primary analysis evaluated the association between ALT flare and HBsAg response. Results 54 subjects were included. 23/54 (43%) subjects experienced an on‐treatment ALT flare. 45% achieved an HBsAg reduction ≥1 log10 IU/mL, and of these 67% achieved HBsAg loss at a median of 102 weeks [IQR: 64‐156]. Flare was associated with HBsAg decline vs non‐response (67% vs 23%, P = .002), and were more common in subjects who achieved HBsAg loss vs non‐response (56% vs 23%), P = .049). There was a median delay of 56 weeks [IQR: 40‐80] between a flare and HBsAg loss. Conclusion In genotype A subjects undergoing long‐term NA therapy, ALT flares predict for HBsAg response. The delay between ALT flare and HBsAg loss has implications for clinical trial design for early phase development of immunomodulatory strategies aiming for HBsAg loss.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".