Economic evaluation of non-invasive liver tests for the diagnosis of liver fibrosis in chronic liver diseases: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to determine the costs and benefits of non-invasive liver tests vs liver biopsy in patients with chronic liver diseases. INTRODUCTION: Hepatic diseases can lead to liver fibrosis, cirrhosis, and hepatocellular carcinoma. In the past, liver biopsy was the only option for diagnosing fibrosis degree. Liver biopsy is an invasive procedure that depends on the sample size to be able to deliver an accurate diagnosis. In recent years, non-invasive liver tests have been increasingly used to estimate liver fibrosis degree; however, there is a lack of economic assessments of technology implementation outcomes. INCLUSION CRITERIA: This review will include partial (cost studies) and complete economic evaluation studies on hepatitis B, hepatitis C, alcoholic liver disease, and non-alcoholic fatty liver disease that compare non-invasive liver tests with liver biopsies. Studies published in English, French, Spanish, German, Italian, or Portuguese will be included. No date limits will be applied to the search. METHODS: This review will identify published and unpublished studies. Published studies will be identified using MEDLINE (PubMed), Cochrane Library (CENTRAL), Embase, Web of Science, Scopus, and LILACS. Sources of unpublished studies and gray literature will include sources from health technology assessment agencies, clinical practice guidelines, regulatory approvals, advisories and warnings, and clinical trial registries, as well as Google Scholar. Two independent reviewers will screen and assess studies, and extract and critically appraise the data. Data extracted from the included studies will be analyzed and summarized to address the review objective using narrative text, and the JBI dominance ranking matrix. REVIEW REGISTRATION: PROSPERO CRD42023404278.
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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 | 0.043 | 0.061 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.004 |
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".