Influence of Liver Inflammation on Liver Stiffness Measurement in Patients with Autoimmune Hepatitis Evaluation by Combinational Elastography
Bibliographic record
Abstract
OBJECTIVE: In order to evaluate the influence of liver inflammation on liver stiffness measurement (LSM) by the simultaneous use of shear wave and strain imaging (combinational elastography), shear wave and strain imaging were compared before and after initial therapy for autoimmune hepatitis (AIH). METHODS: Nine AIH patients initially treated with steroid were enrolled. Transient elastography and real-time tissue elastography were performed just before and 1 month after the start of initial steroid treatment. Blood samples, LSM, and the liver fibrosis index (LFI) were compared. RESULTS: Aspartate aminotransferase (p = 0.002) and alanine aminotransferase (ALT) (p = 0.015) were significantly decreased after initial treatment. The LSM was 15.5 ± 9.6 kPa at baseline, decreasing to 7.2 ± 2.3 kPa after initial treatment p = 0.034). The LFI was 1.67 ± 0.67 at baseline and 1.61 ± 0.66 after initial treatment; no significant change in LFI was recognized (p = 0.842). Between ΔALT and ΔLSM, a significant regression equation could be calculated as follows: ΔALT = -0.55 + 0.654 × ΔLSM. CONCLUSIONS: Combinational elastography was useful in evaluating not only the degree of liver fibrosis, but also the degree of liver inflammation in AIH.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".