Systematic Review and Meta–Analysis of the Diagnostic Accuracy of Fibrosis Marker Panels in Patients with HIV/Hepatitis C Coinfection
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Bibliographic record
Abstract
BACKGROUND: Accurately staging hepatitis C virus (HCV)-related fibrosis is crucial for treatment decisions and prognostication. Our objective was to systematically review studies describing the accuracy of serum marker panels for predicting fibrosis in HIV/HCV-coinfected patients. METHOD: Studies comparing serum marker panels with biopsy in HIV/HCV-coinfected patients were identified. Random effects meta-analyses and areas under summary receiver operating characteristics curves (AUC) examined test accuracy for detecting significant fibrosis (F2-4) and cirrhosis. Heterogeneity was explored using meta-regression. RESULTS: Five studies (n = 574) including four fibrosis measures (APRI [n = 4 studies], Forns' [n = 2], FibroTest [n = 1], SHASTA [n = 1]) met the inclusion criteria. The prevalence of significant fibrosis and cirrhosis were 51% and 16%, respectively. For the prediction of significant fibrosis, the summary AUC was 0.82 (95% CI 0.78-86) and diagnostic odds ratio was 7.8 (5.1-11.9). For cirrhosis, these figures were 0.83 (0.69-0.97) and 11.0 (4.6-26.2), respectively. Meta-regression including study factors (methodological quality and biopsy adequacy), patient characteristics (age, gender, CD4 count), and fibrosis measure failed to identify important predictors of accuracy. CONCLUSION: Available fibrosis marker panels have acceptable performance for identifying significant fibrosis and cirrhosis in HIV/HCV-coinfected patients but are not yet adequate to replace liver biopsy. Additional studies are necessary to identify the optimal measure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.024 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it