Clinical- and Cost-Effectiveness of Liver Disease Staging in Hepatitis C Virus Infection: A Microsimulation Study
Bibliographic record
Abstract
BACKGROUND: Liver disease assessment is a key aspect of chronic hepatitis C virus (HCV) infection pre-treatment evaluation but guidelines differ on the optimal testing modality given trade-offs in availability and accuracy. We compared clinical outcomes and cost-effectiveness of common fibrosis staging strategies. METHODS: We simulated adults with chronic HCV receiving care at US health centers through a lifetime microsimulation across five strategies: (1) no staging or treatment (comparator), (2) indirect serum biomarker testing (Fibrosis-4 index [FIB-4]) only, (3) transient elastography (TE) only, (4) staged approach: FIB-4 for all, TE only for intermediate FIB-4 scores (1.45-3.25), and (5) both tests for all. Outcomes included infections cured, cirrhosis cases, liver-related deaths, costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). We used literature-informed loss to follow-up (LTFU) rates and 2021 Medicaid perspective and costs. RESULTS: FIB-4 alone generated the best clinical outcomes: 87.7% cured, 8.7% developed cirrhosis, and 4.6% had liver-related deaths. TE strategies cured 58.5%-76.6%, 16.8%-29.4% developed cirrhosis, and 11.6%-22.6% had liver-related deaths. All TE strategies yielded worse clinical outcomes at higher costs per QALY than FIB-4 only, which had an ICER of $12 869 per QALY gained compared with no staging or treatment. LTFU drove these findings: TE strategies were only cost-effective with no LTFU. In a point-of-care HCV test-and-treat scenario, treatment without any staging was most clinically and cost-effective. CONCLUSIONS: FIB-4 staging alone resulted in optimal clinical outcomes and was cost-effective. Treatment for chronic HCV should not be delayed while awaiting fibrosis staging with TE.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".