Bibliographic record
Abstract
Magnetic resonance elastography (MRE) is an innovative imaging technique for the non-invasive quantification of the biomechanical properties of soft tissues via the direct visualization of propagating shear waves in vivo using a modified phase-contrast magnetic resonance imaging (MRI) sequence. Fundamentally, MRE employs the same physical property that physicians utilize when performing manual palpation - that healthy and diseased tissues can be differentiated on the basis of widely differing mechanical stiffness. By performing "virtual palpation", MRE is able to provide information that is beyond the capabilities of conventional morphologic imaging modalities. In an era of increasing adoption of multi-parametric imaging approaches for solving complex problems, MRE can be seamlessly incorporated into a standard MRI examination to provide a rapid, reliable and comprehensive imaging evaluation at a single patient appointment. Originally described by the Mayo Clinic in 1995, the technique represents the most accurate non-invasive method for the detection and staging of liver fibrosis and is currently performed in more than 100 centers worldwide. In this general review, the mechanical properties of soft tissues, principles of MRE, clinical applications of MRE in the liver and beyond, and limitations and future directions of this discipline -are discussed. Selected diagrams and images are provided for illustration.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".