A Health Technology Assessment of Transient Elastography in Adult Liver Disease
Bibliographic record
Abstract
BACKGROUND: An estimated one in 10 Canadians have some form of liver disease. The reference standard for staging and monitoring liver fibrosis is percutaneous liver biopsy--an invasive procedure associated with risks and complications. Transient elastography (TE) represents a noninvasive, ultrasound-based alternative. OBJECTIVE: To assess the efficacy of TE compared with liver biopsy for fibrosis staging in adults with five common types of liver disease: hepatitis B, hepatitis C, nonalcoholic fatty liver disease, cholestatic liver disease and complications post-liver transplantation. METHODS: A systematic review of published and grey literature from 2001 to June 2011 was conducted. Included were observational studies evaluating the accuracy of TE using liver biopsy as the comparator. An economic model was developed to estimate the cost per correct diagnosis gained with liver biopsy compared with TE. Identification of moderate fibrosis (stages 2 to 4) and cirrhosis (stage 4) were considered. RESULTS: Fifty-seven studies were included in the review. The diagnostic accuracy of TE for the five clinical subgroups had sensitivities ranging from 0.67 to 0.92 and specificities ranging from 0.72 to 0.95. Liver biopsy was associated with an additional $1,427 to $7,030 per correct diagnosis gained compared with TE. The model was sensitive to the sensitivity and specificity of TE and the prevalence of fibrosis. CONCLUSIONS: TE is an accurate diagnostic method in patients with moderate fibrosis or cirrhosis. TE is less effective but less expensive than liver biopsy. Systemic implementation of TE should be considered for the noninvasive assessment of liver fibrosis.
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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 | 0.035 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".