Accuracy of Velacur in Assessing MASLD and MASH Patients Using Biopsy as the Gold Standard
Bibliographic record
Abstract
Background/Objectives: VelacurTM is a novel, point-of-care ultrasound device developed to accurately diagnose patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and Metabolic Dysfunction-Associated Steatohepatitis (MASH). The Velacur system non-invasively assesses liver stiffness, attenuation, and the Velacurdetermined fat fraction (VDFF). In this study, the performance of Velacur was measured against biopsy results in a cohort of MASLD and MASH patients. Methods: This prospective study enrolled adult patients who were scheduled to undergo biopsy within 6 months of enrollment. The primary objective was to validate Velacur’s findings against that of histological findings. The secondary objective was to compare Velacur results with those of FibroScan. Results: A total of 78 participants were enrolled, and 70 were included in the analysis. Patients had a mean age of 53.3 ± 13.1 years, with a mean BMI of 35.0 ± 6.24 kg/m2. A total of 11, 19, 13, 25, and 2 were characterized as F0 to F4, respectively. The mean Velacur stiffness was 6.48 ± 1.4 kPa, and the mean VDFF was 14.4 ± 5.1%. In patients with significant fibrosis the Velacur AUC [95% CI] was 0.86 [0.76, 0.93] and 0.79 [0.66, 0.88] for patients with advanced fibrosis. For measurements of steatosis, 2, 24, 20, and 24 patients were found to have S0 to S3, respectively. To determine moderate steatosis (≥S2), the VDFF had an AUC of 0.846 [0.716, 0.920]. In the comparison population (n = 59), VDFF (0.85 [0.72, 0.94]) was significantly different than FibroScan CAP (0.50 [0.35, 0.66]) for the detection of moderate steatosis. Conclusions: This study validates the use of Velacur as a non-invasive tool for assessment of steatosis and fibrosis, hallmarks of MASLD and MASH, when compared to histological evidence provided via hepatic biopsy. Further, Velacur outperformed FibroScan in the assessment of steatosis.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".