Diagnosis of esophageal varices by liver stiffness and serum biomarkers in virus-related compensated advanced chronic liver disease
Bibliographic record
Abstract
Background: Individuals infected with hepatitis B (HBV), hepatitis C (HCV), and human immunodeficiency (HIV) viruses can experience compensated advanced chronic liver disease (cACLD) leading to esophageal varices (EV). In patients at low risk of esophageal varices needing treatment (EVNT), non-invasive criteria based on liver stiffness measurement (LSM) with platelets, or fibrosis biomarkers, may avoid unnecessary screening esophagogastroduodenoscopies (EGD). These approaches have not been compared among people infected with HIV, HBV, and HCV patients. Methods: Patients with a diagnosis of cACLD (LSM ≥10 kPa) and EGD availability were included from two cohorts. Baveno VI and expanded Baveno VI criteria (based on LSM and platelets), fibrosis biomarkers Fibrosis-4 Index (FIB-4), AST-to-Platelets Ratio Index (APRI), AST-to-ALT ratio (AAR), and RESIST criteria (based on platelets and albumin) were applied to determine the proportion of spared EGD and of missed EVNT. Results: Three hundred fifty three patients (30.6% with HIV, 25.3% monoinfected with HBV, and 44.1% with HCV) were included. The prevalence of EVNT was 8.2%. Both Baveno VI and expanded Baveno VI criteria performed well in patients with virus-related cACLD, by sparing 26.1% and 51.6% EGD, respectively, while missing <2% EVNT. The proportion of spared EGD were 48.2%, 58%, and 24.3% by FIB-4 (<2.78), APRI (<1.1), and AAR (<0.75), respectively, while missing <3% EVNT. RESIST criteria spared 47.8% EGD while missing 1.9% EVNT. Conclusions: Non-invasive criteria based on LSM can spare unnecessary EGD in virus-related cACLD. Simple fibrosis biomarkers can ameliorate resource utilization for EVNT screening in low resource settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".