AASLD IDSA Practice Guideline on treatment of chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND AND AIMS: Accumulating data related to prevention, surveillance and treatment of chronic hepatitis B (CHB) provided the impetus for this updated guideline, using the Grading of Recommendation Assessment, Development and Evaluation (GRADE) approach. METHODS: The guideline was developed in compliance with the National Academy of Medicine standards. The guideline panel developed structured questions following the Population, Intervention Comparison, Outcomes (PICO) framework. The panel addressed 6 PICO questions covering prevention (maternal to infant transmission and horizontal transmission), surveillance for liver cancer (among hepatitis B surface antigen positive (HBsAg) persons co-infected with hepatitis C virus, hepatitis D virus and/or human immunodeficiency viruses and after HBsAg loss) and treatment (HBsAg positive persons in immune-tolerant or indeterminate phases as well as withdrawal of antiviral therapy), providing evidence-based recommendations on these topics. Four systematic reviews of the literature were conducted, and two existing systematic reviews were utilized to support the recommendations in this practice guideline. CONCLUSIONS: This evidence-based guideline provides updated recommendations to optimize the care of persons with CHB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".