Systematic Review and Meta–Analysis of the Diagnostic Accuracy of Fibrosis Marker Panels in Patients with HIV/Hepatitis C Coinfection
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".