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Record W4410541322 · doi:10.1097/hep.0000000000001407

Management of immune-tolerant chronic hepatitis B

2025· article· en· W4410541322 on OpenAlexaff
Mai Kilany, Milan J. Sonneveld, Jordan J. Feld, Harry L.A. Janssen

Bibliographic record

VenueHepatology · 2025
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsChronic hepatitisMedicineImmune systemImmunologyVirologyVirus

Abstract

fetched live from OpenAlex

The immune-tolerant (IT) phase of chronic hepatitis B is a distinct stage of infection, characterized by high HBV replication, normal ALT levels, and minimal liver inflammation. Despite its classification as a benign phase, growing evidence challenges this notion, revealing immune activation, HBV DNA integration, and potential oncogenic processes even in the absence of elevated ALT. The IT phase's prolonged high viral replication raises concerns about its implications for HCC risk. Histological studies show that significant inflammation and fibrosis may exist in patients meeting IT criteria, suggesting that the current definitions may underestimate disease activity. Treatment during the IT phase remains controversial, with international guidelines largely recommending against antiviral therapy due to its limited efficacy and potential risks. However, subsets of IT patients may benefit from early intervention. The risks and benefits of therapy in IT chronic hepatitis B are not fully understood, and the lack of consensus regarding treatment thresholds further complicates clinical decision-making. This review highlights the importance of redefining IT chronic hepatitis B to include virological and histological parameters and calls for long-term studies to clarify the role of therapy in reducing fibrosis progression and HCC risk. A more precise understanding of the IT phase is essential to balance the risks of treatment against its potential benefits and to inform future therapeutic strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.290
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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