Diagnostic Accuracy of Elastography and Liver Disease: A Meta-Analysis
Bibliographic record
Abstract
Background: Ultrasound-based transient elastography (TE) is a non-invasive alternative to liver biopsy for the staging of hepatic fibrosis due to various chronic liver diseases. This meta-analysis aims to assess the diagnostic accuracy of TE for detecting liver cirrhosis (F4) and severe fibrosis (F3) in patients with chronic liver diseases, in comparison to the gold standard liver biopsy. Methods: A systematic search was performed using PubMed search engine following Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines from inception to May 2021. The meta-analysis studies evaluating the diagnostic accuracy of TE for severe fibrosis and cirrhosis were identified. We conducted a meta-meta-analysis to generate pooled estimates of the sensitivity, specificity, and diagnostic odds ratios (ORs) for F3 and F4 fibrosis stage. Results: We included five studies with a total of 124 sub-studies and 20,341 patients in our analysis. Three studies have reported the diagnostic accuracy of TE in detecting F3/severe fibrosis stage and found 81.9% pooled sensitivity (95% confidence interval (CI): 79.9-83.7%; P < 0.001) (I 2 = 0%), 84.7% pooled specificity (95% CI: 81.3-87.6%) (I 2 = 81%; P = 0.02). All five studies reported the diagnostic accuracy of TE in detecting F4/liver cirrhosis stage. We found 84.8% pooled sensitivity (95% CI: 81.4-87.7%) (I 2 = 86.4%; P < 0.001), 87.5% pooled specificity (95% CI: 85.4-89.3%) (I 2 = 90%; P < 0.001) and pooled diagnostic OR (41.8; 95% CI: 3.9 - 56.5) (I 2 = 87%; P < 0.001). Conclusions: Ultrasound-based TE has excellent diagnostic accuracy for identifying cirrhosis and liver fibrosis stages 3. Future studies should focus on estimating the diagnostic accuracy of other fibrosis stages in chronic liver disease patients. This will eventually decrease the risk associated with invasive liver biopsy. Gastroenterol Res. 2022;15(5):232-239 doi: https://doi.org/10.14740/gr1557
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".