Peginterferon Alfa-2a (40 kD) Stopping Rules in Chronic Hepatitis B: A Systematic Review and Meta-Analysis of Individual Participant Data
Bibliographic record
Abstract
Background Peginterferon alfa-2a (PEG-IFN) treatment stopping rules in chronic hepatitis B (CHB) are clinically desirable. Previous studies exploring this topic contained important limitations resulting in inconsistent recommendations within the current treatment guidelines. We undertook a systematic review and individual patient data meta-analysis to identify the most appropriate PEG-IFN treatment stopping rules. Methods Roche's internal database, PubMed and conference abstracts were searched for studies that enrolled >50 treatment-naive patients with CHB who received PEG-IFN treatment for 48 weeks. Stopping rules were identified using receiver-operating characteristic curve analyses and pre-specified biomarker cutoff target performance characteristics (sensitivity >95%, specificity >10%, negative predictive value >90%). Robustness of proposed stopping rules was assessed using internal/external validation analyses. Results Eight study datasets were included in the meta-analysis ( n=1,423; 765 hepatitis B e antigen [HBeAg]-positive, 658 HBeAg-negative patients). In general, performance of hepatitis B surface antigen (HBsAg) and HBV DNA cutoffs at weeks 12 and 24 was similar, and common biomarker cutoffs that met target performance criteria were identified across multiple patient subgroups. For HBeAg-positive genotype B/C and HBeAg-negative genotype D patients the proposed stopping rule is HBsAg >20,000 IU/ml at week 12. Alternatively, HBV DNA level cutoffs of >8 log 10 and >6.5 log 10 IU/ml, respectively, can be used instead. The proposed stopping rules accurately identify up to 26% of non-responders. Conclusions The meta-analysis demonstrates that early PEG-IFN discontinuation should be considered in HBeAg-positive genotype B/C and HBeAg-negative genotype D patients at week 12 of treatment based on HBsAg or HBV DNA levels.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".