Transient Elastography for the Noninvasive Assessment of Liver Fibrosis: A Multicentre Canadian Study
Bibliographic record
Abstract
BACKGROUND: Liver stiffness measurement (LSM) using transient elastography (TE) is a promising tool for the noninvasive assessment of hepatic fibrosis. OBJECTIVES: To determine the feasibility and performance of TE in a North American cohort of patients with chronic liver disease. METHODS: LSMs were obtained using TE in 260 patients with chronic hepatitis B or C, or nonalcoholic fatty liver disease from four Canadian hepatology centres. The accuracy of TE compared with liver biopsy for the prediction of significant fibrosis (Metavir fibrosis score of F2 or greater), bridging fibrosis (Metavir fibrosis score of F3 or greater) and cirrhosis (Metavir fibrosis score of F4 ) was assessed using area under ROC curves (AUROCs), and compared with the aspartate aminotransferase-to-platelet ratio index. The influence of alanine aminotransferase (ALT) levels and other factors on liver stiffness was determined using linear regression analyses. RESULTS: failure of TE occurred in 2.7% of patients, while liver biopsies were inadequate for staging in 0.8%. Among the remaining 251 patients, the AUROCs of TE for Metavir fibrosis scores of F2 and F3 or greater, and F4 were 0.74 (95% CI 0.68 to 0.80), 0.89 (95% CI 0.84 to 0.94), and 0.94 (95% CI 0.90 to 0.97), respectively. LSM was more accurate than the aminotransferase-to-platelet ratio index for bridging fibrosis (AUROC 0.78) and cirrhosis (AUROC 0.88), but not significant fibrosis (AUROC 0.76). At a cut-off of 11.1 kPa, the sensitivity, specificity, and positive and negative predictive values for cirrhosis (prevalence 11%) were 96%, 81%, 39% and 99%, respectively. For significant fibrosis (prevalence 53%), a cut-off of 7.7 kPa was 68% sensitive and 69% specific, and had a positive predictive value of 70% and a negative predictive value of 65%. Liver stiffness was independently associated with ALT, body mass index and steatosis. The optimal LSM cut-offs for cirrhosis were 11.1 kPa and 11.5 kPa in patients with ALT levels lower than 100 U⁄L and 100 U⁄L or greater, respectively. For fibrosis scores of F2 or greater, these figures were 7.0 kPa and 8.6 kPa, respectively. CONCLUSIONS: the major role of TE is the exclusion of bridging fibrosis and cirrhosis. However, TE cannot replace biopsy for the diagnosis of significant fibrosis. Because liver stiffness may be influenced by significant ALT elevation, body mass index and⁄or steatosis, tailored liver stiffness cut-offs may be necessary to account for these factors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".