Platelet count to spleen diameter ratio non‐invasively identifies severe fibrosis and cirrhosis in patients with autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND AND AIM: Non-invasive markers are essential to assess the progression of chronic liver diseases to fibrosis/ cirrhosis and the effectiveness of therapeutic strategies. The aim of this study was to evaluate the ability of non-invasive markers to identify significant fibrosis, severe fibrosis, and cirrhosis in patients with autoimmune hepatitis (AIH). METHODS: Seventy-six patients with AIH were enrolled in the study and analyzed for the following parameters of liver fibrosis: Fibrosis 4 score (FIB-4), aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio (AAR), AST to platelet count ratio (APRI), and platelet count to spleen diameter (PC/SD) ratio. All patients underwent liver biopsy. The diagnostic accuracy of tests was evaluated by the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the 76 AIH patients, 55 (72.3%) had significant fibrosis (≥ F2), 37 (48.7%) had severe fibrosis (≥ F3), and 29 (38.2%) had cirrhosis (F4). PC/SD ratio (AUROC = 0.840) was superior to AAR (AUROC = 0.756), FIB-4 (AUROC = 0.702), and APRI (AUROC = 0.626) in discriminating between mild and significant fibrosis (≥ F2). The AUROCs of PC/SD ratio, FIB-4, AAR, and APRI were 0.884, 0.742, 0.731, and 0.707, respectively, for severe fibrosis (≥ F3); 0.968, 0.795, 0.744, and 0.723, respectively, for cirrhosis (F4). PC/SD ratio correctly identified 85.1% of patients with severe fibrosis, and 89.6% of patients with cirrhosis. CONCLUSIONS: PC/SD ratio proved to be a simple non-invasive tool to correctly identify AIH patients with severe fibrosis and cirrhosis, thereby reducing the need for a liver biopsy in these patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".