Ultrasound shear wave attenuation in nonalcoholic fatty liver disease: performance of the revisited frequency-shift method with pre-clinical and human datasets
Bibliographic record
Abstract
Assessment of ultrasound shear wave attenuation (SWA) can increase the accuracy of liver steatosis grading. Recent developments allowed to propose the revisited frequency shift method for SWA imaging. This method was tested on force-fed ducks for foie gras production (n = 6), and a feasibility study on patients with non-alcoholic steatohepatitis (NASH n = 27 + 13 healthy volunteers) was conducted. Liver biopsy and magnetic resonance imaging proton density fat fraction (MRI-PDFF) were available as reference standards. For the human study, shear wave dispersion (SWD) was also computed. A subset of participants had repeated measurements (<1-month) to assess repeatability. The mean SWA (coefficient of variation within the attenuation map) for healthy duck livers were 0.77 (0.66), 1.18 (0.22), and 1.52 Np/m/Hz (0.14), and for fatty duck livers they were higher at 3.13 (0.55), 3.16 (0.24), and 4.84 Np/m/Hz (0.23). Receiver operating characteristic (ROC) curves using biopsy as reference were compared for steatosis grades >0 (>S0), >S1, and >S2. SWA was as good as MRI-PDFF, and both were better than SWD for steatosis grading (p < 0.05). The repeatability of SWA was very good (mean intraclass correlation coefficient of 0.97). In summary, SWA seems promising to become an ultrasound reference standard for steatosis grading in NASH.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".