MAFLD considerations as a part of the global hepatitis C elimination effort: an international perspective
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) set a goal to eliminate hepatitis C (HCV) infection globally by 2030, with specific targets to reduce new viral hepatitis infections by 80% and reduce related deaths by 65%. However, an overlooked aspect that may hinder these efforts is the impact other liver diseases could have by continuing to drive liver disease progression and offset the beneficial impact of DAAs on end-stage liver disease and hepatocellular carcinoma (HCC). In particular, the decrease in HCV prevalence has been countered by a marked increase in the prevalence of metabolic-associated fatty liver disease (MAFLD). AIMS: To review the potential interaction of HCV and MAFLD. METHODS: We have reviewed the literature relating to an arrange of interaction of HCV, metabolic dysfunction and MAFLD. RESULTS: In this viewpoint, international experts suggest a holistic and multidisciplinary approach for the management of the growing number of treated HCV patients who achieved SVR, taking into consideration the overlooked impact of MAFLD for reducing morbidity and mortality in people who have had HCV. CONCLUSIONS: This will strengthen and improve the continuum of care cascade for patients with liver disease(s) and holds the potential to alleviate the cost burden of disease; and increase quality of life for patients following DAAs treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".