Effects of on‐treatment ALT flares on serum HBsAg and HBV RNA in patients with chronic HBV infection
Bibliographic record
Abstract
Abstract As pegylated interferon alpha (PEG‐IFN‐α) is increasingly used in combination regimens of novel drugs, we aimed to characterize ALT flares and their relationship with serum HBsAg and HBV RNA kinetics in a large combined cohort of chronic hepatitis B (CHB) patients on PEG‐IFN‐α‐based therapy. In this post hoc analysis of four international randomized trials, 269/130/124/128 patients on PEG‐IFN‐α monotherapy, PEG‐IFN‐α plus nucleos(t)ide analogue (NA) de novo combination, PEG‐IFN‐α add‐on to NA or NA monotherapy were included, respectively. A flare was defined as an episode of ALT ≥5 × ULN. The association between flares and HBsAg and HBV RNA changes were examined. On‐treatment flares occurred in 83/651 (13%) patients (median timing/magnitude: week 8 [IQR 4–12], 7.6 × ULN [IQR 6.2–10.5]). Flare patients were more often Caucasians with genotype A/D and had higher baseline ALT, HBV DNA, HBV RNA and HBsAg levels than the no‐flare group. More flares were observed on PEG‐IFN‐α monotherapy (18%) and PEG‐IFN+NA de novo combination (24%) vs. PEG‐IFN‐α add‐on (2%) or NA monotherapy (1%) (p < .001). On‐treatment flares were significantly and independently associated with HBsAg and HBV RNA decline ≥1 log10 at the final visit declines started shortly before the flare, progressing towards 24 weeks thereafter. On‐treatment flares were seen in 16/22 (73%) patients who achieved HBsAg loss. In conclusion, ALT flares during PEG‐IFN‐α treatment are associated with subsequent HBsAg and HBV RNA decline and predict subsequent HBsAg loss. Flares rarely occurred during PEG‐IFN‐α add‐on therapy and associated with low HBsAg loss rates. Combination regimens targeting the window of heightened response could be promising.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".