Diagnostic utility of radiological heterogeneity in acute severe (fulminant) autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND: Histological examination is useful for the diagnosis of acute severe (fulminant) autoimmune hepatitis (AIH), but it is sometimes difficult to perform liver biopsy due to the complicated coagulopathy and ascites. We have shown that heterogeneous hypoattenuation on unenhanced computed tomography (CT) is a characteristic imaging feature of acute severe (fulminant) AIH. In the present study, we examined the utility of the imaging feature by applying the score to diagnose acute severe (fulminant) AIH. METHODS: Twenty-three patients with acute severe (fulminant) AIH were analyzed retrospectively. Modified AIH score was created by adding three points to AIH score with/without histological points in case of the presence of heterogeneous hypoattenuation on unenhanced CT. RESULTS: Areas of hypoattenuation were present in 15 (65%) patients, all of which were heterogeneous pattern. Five (22%) patients were diagnosed as "definite" AIH, 16 (69%) as "probable" and two (9%) as "non-diagnosis" by the revised original score without histological score. By adding three points, two of "non-diagnosis" changed to "probable" AIH, and all patients were diagnosed as AIH. CONCLUSIONS: Modified AIH score using heterogeneous CT image finding would be beneficial especially for patients in whom histological examinations cannot be performed because of complications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".