Two-Tier Care Pathways for Liver Fibrosis Associated to Non-Alcoholic Fatty Liver Disease in HIV Mono-Infected Patients
Bibliographic record
Abstract
(1) Background: Developing strategies to identify significant liver fibrosis in people with HIV (PWH) is crucial to prevent complications of non-alcoholic fatty liver disease (NAFLD). We aim to investigate if five simple serum biomarkers applied to PWH can optimize a care pathway to identify significant liver fibrosis defined by transient elastography (TE). (2) Methods: A two-tier fibrosis pathway was applied to three prospective cohorts of PWH undergoing TE with CAP. NAFLD was diagnosed as a controlled attenuation parameter ≥ 248 dB/m. Five simple fibrosis biomarkers (FIB-4 < 1.3, BARD score 0-1, NAFLD fibrosis score < -1.455, AST:ALT ratio < 0.8 and APRI < 0.5) were applied as first-tiers to exclude significant liver fibrosis. We determined the decrease in referral for TE that would have occurred based on biomarker assessment and the discordance between low simple fibrosis biomarkers and high TE (≥7.1 kPa), indicating significant liver fibrosis. (3) Results: Of the 1749 consecutive PWH, 15.1% had significant liver fibrosis by TE and 39.1% had NAFLD. The application of the fibrosis biomarkers as first tiers would have resulted in a decrease in TE referrals between 24.9% (BARD score) and 86.3% (APRI). The lowest discordance rate was with NAFLD fibrosis score (8.5%). After adjustments, BMI (odds ratio (OR) 1.12, 95% CI: 1.08-1.17) and triglycerides (OR 1.26, 95% CI: 1.11-1.44) were independent predictors of discordance for APRI < 0.5 and TE ≥ 7.1. The performance of the two-tier pathways was similar in PWH with and without NAFLD. (4) Conclusions: Implementing a two-tier pathway could save a substantial proportion up of TE examinations, reducing costs and helping resource optimization in HIV care. Patients with metabolic risk factors for NAFLD and low fibrosis biomarker may still be considered for TE referral.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".