HAL (France)
open access
Diagnostic accuracy of non‐invasive tests to screen for at‐risk MASH —An individual participant data meta‐analysis
Abstract Background & Aims There is a need to reduce the screen failure rate (SFR) in metabolic dysfunction‐associated steatohepatitis (MASH) clinical trials (MASH+F2‐3; MASH+F4) and identify people with high‐risk MASH (MASH+F2‐4) in clinical practice. We aimed to evaluate non‐invasive tests (NI
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Metabolic Flux Analysis in Metabolic Dysfunction-Associated Steatotic Liver Disease#1350217
- <b>Poster presented at EASL 2026: </b>Metabolic matters: real-world assessment of metabolic dysfunction–associated steatotic liver diseases in the United States by integrating international classification of diseases codes and non-invasive tests for improved detection#226644
- Non-invasive fibrosis testing in adults with obesity and MASLD: a systematic review and meta-analysis#989979
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.#685641
- Non-Alcoholic Fatty Liver Disease in a Saudi Cohort With Type 2 Diabetes Mellitus#854398
Record · ID 1470139
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive