<scp>APRI</scp> and <scp>FIB</scp>‐4 are good predictors of the stage of liver fibrosis in chronic hepatitis <scp>B</scp>: the Chronic Hepatitis Cohort Study (<scp>CH</scp>e<scp>CS</scp>)
Bibliographic record
Abstract
We aim to determine the predictive ability of APRI, FIB-4 and AST/ALT ratio for staging of liver fibrosis and to differentiate significant fibrosis (F2-F4) from none to minimal fibrosis (F0-F1) in chronic hepatitis B (CHB). Liver biopsy results were mapped to an F0-4 equivalent fibrosis stage. Mean APRI and FIB-4 scores were significantly higher for each successive fibrosis level from F1 to F4 (P < 0.05). Based on optimized cut-offs, the AUROCs in distinguishing F2-F4 from F0 to F1 were 0.81 (0.76-0.87) for APRI, 0.81 (0.75-0.86) for FIB-4 and 0.56 (0.49-0.64) for AST/ALT ratio. APRI and FIB-4 distinguished F2-F4 from F0 to F1 with good sensitivity and specificity and can be useful for treatment decisions and monitoring progression of fibrosis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".