Mean HBsAg decline at week 24 of PEG‐IFN‐based treatment predicts subsequent rate of HBsAg clearance — suggesting a valuable endpoint for early development HBV trials
Bibliographic record
Abstract
Abstract Earlier identification of potentially efficacious treatments in early development trials requires on‐treatment response markers. We hypothesized that mean week 12 or 24 HBsAg decline could be a useful marker for subsequent off‐treatment sustained HBsAg clearance at the treatment arm level in HBV trials. We used individual patient data from the studies HBV 9901 (peginterferon [PEG‐IFN] versus PEG‐IFN+lamivudine for HBeAg‐positive CHB), PARC (PEG‐IFN±ribavirin for HBeAg‐negative CHB) and published data from 0149 (PEG‐IFN±tenofovir for HBeAg‐positive and HBeAg‐negative CHB) and LIRA‐B (PEG‐IFN for HBeAg‐positive CHB) to define the relationship between mean week HBsAg decline and HBsAg loss at 6 months post‐treatment. A within‐study comparison of HBsAg decline at weeks 12 and 24 between patients with or without HBsAg clearance was used to make projections beyond the observed HBsAg data. Across trials, a more pronounced mean HBsAg decline at week 24 was associated with higher rates of subsequent HBsAg loss. Mean HBsAg decline data at week 24 for patients with or without HBsAg clearance from HBV 9901 (4.3 vs 0.5), PARC (4.8 vs 0.3) and 0149 (PEG‐IFN+TDF arm; 4.6 vs 0.6) were used to extrapolate this relationship beyond observed rates of HBsAg. An additional mean 1 log10 decline at week 24 versus a comparator arm is expected to translate into a 20%–30% increase in subsequent HBsAg loss during off‐treatment follow‐up. Observations were similar for week 12 data, but the relationship was less strong. Mean week 24 HBsAg decline predicts subsequent HBsAg loss and could be a valuable and useful early endpoint in HBV‐treatment trials.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".