Accuracy of transient elastography in the assessment of chronic hepatitis C-related liver cirrhosis
Bibliographic record
Abstract
PURPOSE: Staging liver cirrhosis is essential for the management of chronic hepatitis C (CHC). The current meta-analysis evaluated the accuracy of transient elastography for detecting liver cirrhosis in patients with CHC. METHODS: Either prospective or retrospective studies, including cohort and cross sectional studies, in patients diagnosed with chronic hepatitis C, as assessed by transient elastography, were searched from Medline, Cochrane, EMBASE, and Google Scholar databases until March 3, 2015, using the terms "transient elastography, chronic hepatitis C and liver cirrhosis". The primary outcome analyzed was the diagnostic performance, which included sensitivity, specificity, diagnostic odds ratio and area under the receiver-operating characteristic (ROC) curve. RESULTS: Data from 24 articles included in the meta-analysis demonstrated high sensitivity (84%) and specificity (90%) of transient elastography (TE) for assessing liver cirrhosis patients with HCV. Subgroup analysis of patients by underlying diseases revealed a sensitivity and specificity of 91% and 92% (HCV alone), 100% and 75% (HCV-liver transplant), 83.6% and 89.7% (HIV/HCV co-infection) and 97.1% and 90.7% (recurrent CHC after liver transplantation). The pooled diagnostic odds ratio was 61.57 (95% CI, 39.5 - 96.00) and the area under the summary ROC curves was 0.952 ± 0.008, suggesting high diagnostic accuracy of TE. CONCLUSION: Transient elastography can accurately predict liver cirrhosis in patients with hepatitis C, with a sensitivity and specificity of 84% and 90%, respectively. The present results further validate the utility of TE in staging liver cirrhosis in chronic HCV infections.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".