Transient elastography for the diagnosis of liver fibrosis: a systematic review of economic evaluations
Bibliographic record
Abstract
BACKGROUND: Liver biopsy remains the gold standard for the diagnosis of liver fibrosis, but its use as a diagnostic tool is limited by its invasive nature and high cost. OBJECTIVE: The aim of this study was to systematically review the cost-effectiveness of transient elastography (TE) with and without controlled attenuation parameter (CAP) for the diagnosis of liver fibrosis or steatosis in patients with hepatitis B, hepatitis C, alcoholic liver disease and non-alcoholic fatty liver disease. METHODS: An economic literature search was performed. Eligibility criteria included systematic reviews, health technology assessments or economic evaluations of TE compared to liver biopsy and other non-invasive tests. After abstract screening, full-text reports of potentially relevant articles were assessed in duplicate. The methodological quality of the included studies was also appraised. RESULTS: The database search yielded 253 records; four cost-effectiveness and four cost-utility studies were included. The methodological quality of the included studies varies. High-quality cost-effectiveness studies not only suggested that TE is less costly but also less accurate than liver biopsy. The incremental cost-effectiveness ratio (ICER) of TE improves with a greater level of diagnostic accuracy and a higher degree of liver fibrosis. High-quality cost-utility studies indicated that TE is a cost-effective alternative to biopsy with ICER between $9000 and $14 000 per QALY for patients with hepatitis C. We did not find studies that assessed the cost-effectiveness of TE with CAP for the diagnosis of liver steatosis. CONCLUSIONS: Transient elastography is an economically attractive alternative to liver biopsy and other non-invasive diagnostic tests especially for patients with a higher degree 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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".