Update to the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement
Bibliographic record
Abstract
This multidisciplinary update of the Society of Radiologists in Ultrasound consensus statement on liver elastography incorporates the large volume of new information available in the literature since the initial publication. The recommended procedure for acquiring stiffness measurements is reviewed. There has been substantial improvement in the acoustic radiation force impulse (ARFI) technology-most notably the addition of a quality assessment of the shear wave propagation. Due to the efforts of the Quantitative Imaging Biomarkers Alliance, or QIBA, the variability of liver stiffness measurements between systems had decreased. There are now effective treatments for hepatitis B and hepatitis C, and follow-up after effective treatment should be based on the use of the delta change of the value obtained at viral eradication or suppression. Because the detection of compensated advanced chronic liver disease (cACLD) is very important, the new guidelines are made based on the probability of cACLD for given stiffness values. The panel recommends a vendor-neutral rule of four for interpretation for ARFI techniques. This new method simplifies interpretation of liver stiffness results and is more clinically relevant.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".