Estimating chronic hepatitis C prognosis using transient elastography‐based liver stiffness: A systematic review and meta‐analysis
Bibliographic record
Abstract
Chronic hepatitis C (CHC) is a leading cause of hepatic fibrosis and cirrhosis. The level of fibrosis is traditionally established by histology, and prognosis is estimated using fibrosis progression rates (FPRs; annual probability of progressing across histological stages). However, newer noninvasive alternatives are quickly replacing biopsy. One alternative, transient elastography (TE), quantifies fibrosis by measuring liver stiffness (LSM). Given these developments, the purpose of this study was (i) to estimate prognosis in treatment-naïve CHC patients using TE-based liver stiffness progression rates (LSPR) as an alternative to FPRs and (ii) to compare consistency between LSPRs and FPRs. A systematic literature search was performed using multiple databases (January 1990 to February 2016). LSPRs were calculated using either a direct method (given the difference in serial LSMs and time elapsed) or an indirect method given a single LSM and the estimated duration of infection and pooled using random-effects meta-analyses. For validation purposes, FPRs were also estimated. Heterogeneity was explored by random-effects meta-regression. Twenty-seven studies reporting on 39 groups of patients (N = 5874) were identified with 35 groups allowing for indirect and 8 for direct estimation of LSPR. The majority (~58%) of patients were HIV/HCV-coinfected. The estimated time-to-cirrhosis based on TE vs biopsy was 39 and 38 years, respectively. In univariate meta-regressions, male sex and HIV were positively and age at assessment, negatively associated with LSPRs. Noninvasive prognosis of HCV is consistent with FPRs in predicting time-to-cirrhosis, but more longitudinal studies of liver stiffness are needed to obtain refined estimates.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".