Validation of a simple model for predicting liver fibrosis in HIV/hepatitis C virus‐coinfected patients
Bibliographic record
Abstract
OBJECTIVES: Recently, several models incorporating laboratory measurements have been validated for use as surrogate markers for liver fibrosis in hepatitis C virus (HCV) mono-infection, the simplest of these being the aspartate aminotransferase (AST) to platelet ratio index (APRI). We evaluated how well the APRI predicts significant hepatic fibrosis in patients with HIV/HCV coinfection. METHODS: Forty-six HIV/HCV-coinfected patients who underwent liver biopsy and had concomitant laboratory measurements (+/-3 months) were included in the study. Significant fibrosis was defined as F2-F4 using Batt and Ludwig scoring (=3 Ishak). APRI=[(AST/upper limit of normal)/platelet count (10(9)/L)] x 100. We used sas proc logistic (SAS Institute, Cary, NC) to calculate the area under the receiver operating curve (ROC) (AUC). Sensitivities, specificities, positive predictive value (PPV) and negative predictive value (NPV) were compared using cut-offs previously identified in the literature. RESULTS: Thirty-three of 46 patients (72%) had significant fibrosis on biopsy. For significant fibrosis, the area under the ROC for the APRI was 0.847+/-0.057. APRI scores >1.5 (the higher cut-off) were 100% specific and 52% sensitive; PPV was 100% and NPV 45%. Scores <0.5 (the lower cut-off) were 82% sensitive and 46% specific in ruling out significant fibrosis (PPV 79%; NPV 50%). CONCLUSIONS: A simple model incorporating readily available laboratory data is highly predictive of significant fibrosis in HIV/HCV coinfection and could serve as a biopsy-sparing measure, thus making treatment more accessible for this population.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".