Utility of shear‐wave elastography to differentiate low from advanced degrees of liver fibrosis in patients with hepatitis C virus infection of native and transplant livers
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of shear-wave elastography (SWE) to differentiate low from advanced degrees of liver fibrosis in hepatitis C patients. MATERIAL & METHOD: Consented native/transplant hepatitis C patients underwent SWE using a C1-6 MHz transducer before ultrasound (US)-guided liver biopsy. Five interpretable SWE samples were obtained from the right lobe of the liver immediately before US-guided random biopsy of the right lobe. Average kilopascal (kPa) values were compared to the meta-analysis of histological data in viral hepatitis (METAVIR) fibrosis grading. SWE values were correlated with the degree of inflammation and fatty infiltration. RESULTS: Study population consisted of 115 patients (63 with transplant, and 52 with native liver) including 29 women and 86 men, with a mean ± SD age of 56 ± 8.7 years. Mean ± SD SWE values were 7.9 ± 3 kPa in 83 patients with METAVIR scores of 0-2 and 13.2 ± 5.9 kPa in 32 patients with METAVIR scores of 3 or 4 (P < .001). Area under curve (AUC) of a Receiver Operating Characteristics curve for advanced degrees of fibrosis was 0.81 (95% CI: 0.71, 0.90) (P < .001). AUCs of transplant versus native livers (0.78 [CI:0.62, 0.94] versus 0.85 [CI: 0.73, 0.96]), degree of inflammation (0.81 [CI: 0.65, 0.97] versus 0.72 [0.56, 0.88]), or degree of fat deposition (0.81 [CI:0.70, 0.92] versus 0.80 [CI:0.61, 1]) were not statistically different (P > .05). for kPa threshold of SWE value of 10.67 kPa to differentiate advanced from low degree of fibrosis had a sensitivity of 59% (CI: 41%-76%) and specificity of 90% (CI: 82%-96%). CONCLUSION: Liver stiffness evaluated by SWE can differentiate low from advanced liver fibrosis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".