Comparison of Liver Biopsy and Transient Elastography based on Clinical Relevance
Bibliographic record
Abstract
BACKGROUND: Liver stiffness measurement (LSM) by transient elastography has recently been validated for the evaluation of liver fibrosis in chronic liver diseases. The present study focused on cases in which liver biopsy and LSM were discordant. METHODS: Three hundred eighty-six patients with chronic hepatitis C who underwent a liver biopsy between December 2004 and April 2007 were studied. First, the optimal cut-off value of LSM was selected for the determination of cirrhosis based on the receiver operating characteristic curve. Then, the cases in which liver histology and evaluation by LSM were discordant were selected. Laboratory test results such as serum total bilirubin concentration, prothrombin activity, albumin concentration, platelet count and the aspartate aminotransferase to platelet ratio index, together with the presence of esophageal varices, were analyzed. RESULTS: The optimal cut-off value was chosen to be 15.9 kPa for cirrhosis (fibrosis stage [F] 4) determination to maximize the sum of sensitivity (78.9%) and specificity (81.0%). There were 78 discordant cases: 51 patients showed an LSM of 15.9 kPa or higher and a fibrosis stage of F1 to F3 (high LSM group), and 27 patients had an LSM lower than 15.9 kPa and a fibrosis stage of F4 (low LSM group). Esophageal varices were seen in 11 patients in the high LSM group (n=51) and in no patients in the low LSM group (n=27) (P=0.0012). The aspartate aminotransferase to platelet ratio index was significantly higher in the high LSM group (1.49 versus 0.89, P=0.019). Other parameters did not differ significantly. However, platelet count, prothrombin activity and albumin concentration tended to be lower in the high LSM group. CONCLUSIONS: Patients with a high LSM need proper attention for cirrhosis, even if liver biopsy does not reveal cirrhosis.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".