Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection†‡
Bibliographic record
Abstract
Liver biopsy remains the gold standard in the assessment of severity of liver disease. Noninvasive tests have gained popularity to predict histology in view of the associated risks of biopsy. However, many models include tests not readily available, and there are limited data from patients with HIV/hepatitis C virus (HCV) coinfection. We aimed to develop a model using routine tests to predict liver fibrosis in patients with HIV/HCV coinfection. A retrospective analysis of liver histology was performed in 832 patients. Liver fibrosis was assessed via Ishak score; patients were categorized as 0-1, 2-3, or 4-6 and were randomly assigned to training (n = 555) or validation (n = 277) sets. Multivariate logistic regression analysis revealed that platelet count (PLT), age, AST, and INR were significantly associated with fibrosis. Additional analysis revealed PLT, age, AST, and ALT as an alternative model. Based on this, a simple index (FIB-4) was developed: age ([yr] x AST [U/L]) / ((PLT [10(9)/L]) x (ALT [U/L])(1/2)). The AUROC of the index was 0.765 for differentiation between Ishak stage 0-3 and 4-6. At a cutoff of <1.45 in the validation set, the negative predictive value to exclude advanced fibrosis (stage 4-6) was 90% with a sensitivity of 70%. A cutoff of >3.25 had a positive predictive value of 65% and a specificity of 97%. Using these cutoffs, 87% of the 198 patients with FIB-4 values outside 1.45-3.25 would be correctly classified, and liver biopsy could be avoided in 71% of the validation group. In conclusion, noninvasive tests can accurately predict hepatic fibrosis and may reduce the need for liver biopsy in the majority of HIV/HCV-coinfected patients.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".