Methods and validation of velacur determined fat fraction in patients with MASLD
Bibliographic record
Abstract
Introduction: As prevalence of patients with steatotic liver diseases increases throughout the world, it is necessary to have accurate and accessible methods to estimate liver fat content. Using quantitative ultrasound parameters, such as attenuation and backscatter, it is possible to estimate liver fat, with MRI proton density fat fraction as the reference standard. Velacur determined fat fraction (VDFF) is a new output measurement on Velacur (Sonic Incytes Medical Corp, Vancouver, BC). Methods: This study described the results of parameter fitting and validation of VDFF, which is a combination of quantitative ultrasound parameters. Patients were recruited from sites within the US and Canada. All patients had contemporaneous Velacur and MRI proton density fat fraction scans. The quantitative ultrasound parameter fitting was completed using linear regression on a random sub-sample approach, and a separate cohort was used for validation. The AUC for detection of 5% liver fat based on MRI-PDFF and the correlation between MRI-PDFF and VDFF was measured in both cohorts. Results: VDFF had an AUROC of 0.97 for the detection of MRI-PDFF > 5% in the parameter fitting cohort, and 0.99 in the validation cohort. The correlation [95% CI] between MRI-PDFF and VDFF was r = 0.84 [0.78 - 0.89] for the parameter fitting cohort and r = 0.90 [0.82 - 0.95] for the validation cohort. Conclusion: The Velacur Determined Fat Fraction (VDFF) is an accurate and accessible way to estimate steatosis as measured by MRI-PDFF. Velacur VDFF can fill the unmet need of an accurate means to diagnosis hepatic steatosis and serve as a potential alternative to biopsy or MRI-PDFF.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".