Diagnostic accuracy of the aspartate aminotransferase-to-platelet ratio index for the prediction of hepatitis C–related fibrosis: A systematic review
Bibliographic record
Abstract
UNLABELLED: The development of noninvasive markers of liver fibrosis is a clinical and research priority. The aspartate aminotransferase-to-platelet ratio index (APRI) is a promising tool with limited expense and widespread availability. Our objective was to systematically review the performance of the APRI in hepatitis C virus (HCV)-infected patients. Random effects meta-analyses and areas under summary receiver operating characteristic curves (AUC) were examined to characterize APRI accuracy for significant fibrosis (stages 2-4) and cirrhosis. In 22 studies (n = 4,266), the summary AUCs of the APRI for significant fibrosis and cirrhosis were 0.76 [95% confidence interval (CI), 0.74-0.79] and 0.82 (95%CI, 0.79-0.86), respectively. For significant fibrosis, an APRI threshold of 0.5 was 81% sensitive and 50% specific. At a 40% prevalence of significant fibrosis, this threshold had a negative predictive value (NPV) of 80%, but could reduce the necessity of liver biopsy by only 35%. For cirrhosis, a threshold of 1.0 was 76% sensitive and 71% specific. At a 15% cirrhosis prevalence, the NPV of this threshold was 91%. Higher APRI thresholds had suboptimal positive predictive values except in settings with a high prevalence of cirrhosis. APRI accuracy was not affected by the prevalence of advanced fibrosis, or study and biopsy quality. However, the accuracy for cirrhosis was greater in studies including human immunodeficiency virus (HIV)/HCV-co-infected patients. CONCLUSION: The major strength of the APRI is the exclusion of significant HCV-related fibrosis. Future studies of novel markers should demonstrate improved accuracy and cost-effectiveness compared with this economical and widely available index.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".