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Record W2589508623 · doi:10.14740/gr754w

Antiviral Therapy in Chronic Hepatitis B With Mild Acute Exacerbation

2017· article· en· W2589508623 on OpenAlexvenueno aff
Su Lin, Qiaoxia Ye, Ming-Fang Wang, Yinlian Wu, Zhiyuan Weng, Yueyong Zhu

Bibliographic record

VenueGastroenterology Research · 2017
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHBsAgHBeAgSeroconversionEntecavirInternal medicineExacerbationTelbivudineGastroenterologyHepatitis BHepatitis B virusChronic hepatitisImmunologyVirusLamivudine

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to assess the efficacy and safety of peginterferon α-2a (pegIFN) and nucleos(t)ide analogues (NA) treatments in patients with hepatitis B envelope antigen (HBeAg)-positive chronic hepatitis B (CHB) with mild acute exacerbation (AE). METHODS: Treatment-naive HBeAg-positive CHB patients with AE who received pegIFN or NA (entecavir (ETV) or telbivudine (LDT)) therapies were retrospectively selected. The HBeAg seroconversion rate, hepatitis B surface antigen (HBsAg) loss rate and the cost-effectiveness of different treatments were compared. RESULTS: A total of 63 patients with pegIFN therapy and 78 with NA (38 with ETV and 40 with LDT) therapy were included. The HBsAg loss rate was significantly higher in the pegIFN group when compared with the NA group (on week 96: 9/63 (14.29%) vs. 1/78 (1.28%), P = 0.005). No significant difference in hepatitis B virus (HBV) DNA negativity or the HBeAg/HBsAg seroconversion rate was found between ETV and LDT group. One year of pegIFN therapy resulted in 18.56 quality-adjusted life years (QALYs) per patient, and the incremental cost per additional QALY gained was $3,709. CONCLUSIONS: PegIFN therapy is safe in HBeAg-positive CHB patients with mild AE, as it results in a higher HBsAg loss rate and longer QALYs than NA therapy.

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)0.001
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 score0.046
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.397
Teacher spread0.318 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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