Laboratory‐based scoring system for prediction of hepatic inflammatory activity in patients with autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND & AIMS: In autoimmune hepatitis (AIH), inflammation is closely related to fibrosis. Although transaminase levels are commonly used to assess hepatic inflammation, they may not relate directly to the histology. We developed a noninvasive diagnostic score as an alternative to liver biopsy to help optimize treatment for AIH and monitor disease progress. METHODS: Eighty-two participants with type 1 AIH who had undergone liver biopsy were included (44 in training and 38 in validation sets). Liver histology was assessed according to the histologic activity index (HAI; score 0-18) and Ishak's histologic fibrosis index (HFI; score 0-6). High inflammation was defined as HAI>4, and advanced fibrosis was defined as HFI>2. Routine laboratory test findings and stepwise linear regression were used to develop the best models predicting HAI and HFI. The best cut-off value to predict high inflammation and advanced fibrosis for these formulas was then calculated based on receiver-operating characteristic analysis. RESULTS: The cut-off value for a model predicting high inflammation was ≥3.57 (AUROC = 0.93; 95% CI: 0.86-1.00), with 100% sensitivity and 85% specificity. High inflammation was confirmed with an 81% positive predictive value and excluded with a 100% negative predictive value. In the validation set, the sensitivity, specificity, positive predictive value and negative predictive values were 100, 56, 88 and 100% respectively. The diagnostic yield of the fibrosis score was unsatisfactory. CONCLUSIONS: The noninvasive inflammatory score based on four routine laboratory parameters discriminated patients with and without significant hepatic inflammation and may facilitate follow-up of type 1 AIH 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) | 0.000 | 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".