The role of noninvasive biomarkers for monitoring cell injury in advanced liver fibrosis
Bibliographic record
Abstract
INTRODUCTION: Accurate and reliable diagnosis and monitoring of hepatic fibrosis is increasingly important given the variable natural history in chronic liver disease (CLD) and expanding antifibrotic therapeutic options targeting reversibility of early-stage cirrhosis. This highlights the need to develop more refined and effective noninvasive techniques for the dynamic assessment of fibrogenesis and fibrolysis. AREAS COVERED: We conducted a literature review on PubMed, from 1 December 1970, to 1 November 2024, to evaluate and compare available blood-based and imaging-based noninvasive tools for hepatic fibrosis diagnosis and monitoring. Simple scores such as FIB-4 and NAFLD fibrosis score are suitable for excluding significant or advanced fibrosis, while tertiary centers should adopt complex scores and liver stiffness measurement as part of a secondary diagnostic and more comprehensive evaluation. Moreover, the advent of multiomics for high-resolution molecular profiling, and integration of artificial intelligence for noninvasive diagnostics holds promise for revolutionizing fibrosis monitoring and treatment through novel biomarker discovery and predictive omics-based algorithms. EXPERT OPINION: The increased shift toward noninvasive diagnostics for liver fibrosis needs to align with personalized medicine, enabling more effective, tailored management strategies for patients with liver disease in the future.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".