Evaluating Liver Fibrosis Progression and the Impact of Antiretroviral Therapy in HIV and Hepatitis C Coinfection Using a Noninvasive Marker
Bibliographic record
Abstract
The effects of highly active antiretroviral therapy (HAART) on progression of hepatic fibrosis in hepatitis C virus (HCV) coinfection with HIV are not well understood and are difficult to measure because of the need for repeated liver biopsy. We evaluated the evolution of a noninvasive measure of liver fibrosis, the alanine aspartyl transferase (AST)-to-platelet ratio index (APRI), longitudinally and determined its predictive value for hepatic outcomes in HIV-positive patients with and without HCV coinfection. A total of 673 HIV-positive patients without liver complications at baseline (540 with HIV only, 133 with HIV-HCV coinfection) were followed between 1991 and 2004 for a median of 4.6 years (3524 person-years). At baseline, HIV-HCV coinfection had a higher median APRI compared with HIV infection alone (0.59 vs. 0.33; P < 0.0001). The natural logarithm of the APRI [ln(APRI)] changed significantly over time, particularly among patients with HIV-HCV coinfection. The baseline ln(APRI) was predictive of liver complications (hazard ratio [HR] = 4.0, 95% confidence interval [CI]: 2.5 to 6.4 per log), as was HCV (HR = 4.5, 95% CI: 1.5 to 14). Cumulative HAART did not protect against liver complications, although it was significantly associated with progression of APRI scores in HIV-HCV coinfection and in HIV alone. In conclusion, the APRI may be a useful marker for longitudinal evaluation of the progression of liver disease in HIV-HCV coinfection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".