Non-Invasive Reliable Methods to Objectify the Positive Influence of Hepatitis C Virus Treatment on Liver Stiffness
Bibliographic record
Abstract
BACKGROUND: Chronic active hepatitis C virus (HCV) infection is a major public health problem and causes liver fibrosis (LF) up to liver cirrhosis (LC). LF can be estimated by non-invasive, easy handling methods. With implementation of new HCV therapies, elimination rates of HCV are near 100%, resulting in less clinical complications and costs. The aim of our study was to evaluate the positive influence of HCV treatment on liver stiffness by non-invasive assessments of LF. METHODS: Sixty-two patients with HCV were treated with antiviral drug regimes. Serological fibrosis scores and ultrasound elastography (acoustic radiation force impulse and shear wave elasticity imaging (ARFI-SWEI)) were used for LF assessment on day 0 and 6 months after therapy. RESULTS: Antiviral treatment was successful in all cases. ARFI-SWEI measurements showed an improvement of all LF stages. Results of serological markers and scores were heterogeneous. Significant positive effects of treatment were seen for aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) scores, only. Further Pearson's coefficient showed moderate till very high correlations for ARFI-SWEI and FIB-4/APRI scores. CONCLUSION: Today HCV therapy is able to cure HCV. Positive influences are improvement of LF stages. ARFI-SWEI, APRI and FIB-4 score are useful, easy handling tools to verify positive influence of HCV treatment on LF alone or in combination.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".