Supersonic shear imaging for the diagnosis of liver fibrosis and portal hypertension in liver diseases: a meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The meta-analysis aimed to summarize the technical success rate of supersonic shear imaging (SSI) and to evaluate the diagnostic performance of liver and spleen stiffness measurement (LSM and SSM) with SSI for the detection of liver fibrosis, portal hypertension, and gastroesophageal varices in liver diseases. METHODS: PubMed, EMBASE, and Cochrane Library databases were searched. Technical success rate of SSI was pooled. Area under curve (AUC), sensitivity, and specificity with corresponding 95% confidence interval (CI) were calculated. RESULTS: Included studies regarding the diagnostic performance of SSI for liver fibrosis, portal hypertension, and esophageal varices numbered 28, 4, and 4 respectively. The pooled technical success rates of LSM and SSM were 95.3% and 75.5%, respectively. The AUC, sensitivity, and specificity of LSM/SSM for different stages of liver fibrosis were 0.85-0.94, 0.7-0.89, and 0.82-0.92, respectively. The AUC, sensitivity, and specificity of LSM were 0.84 (95%CI = 0.8-0.86), 0.79 (95%CI = 0.7-0.85), and 0.82 (95%CI = 0.72-0.88) for clinically significant portal hypertension, 0.85 (95%CI = 0.82-0.88), 0.8 (95%CI = 0.68-0.88), and 0.8 (95%CI = 0.6-0.92) for any varices, and 0.86 (95%CI = 0.83-0.89), 0.86 (95%CI = 0.76-0.92), and 0.61 (95%CI = 0.35-0.83) for high-risk varices, respectively. CONCLUSIONS: LSM with SSI had a high diagnostic accuracy for liver fibrosis, but a moderate diagnostic accuracy for portal hypertension and esophageal varices.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.039 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".