Hepatic ultrastructural features distinguish paediatric Wilson disease from <scp>NAFLD</scp> and autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Wilson disease (WD) has diverse presentations that frequently mimic other liver diseases. Distinguishing WD from non-alcoholic fatty liver disease (NAFLD) and autoimmune hepatitis (AIH), can be difficult and has critical implications for medical management. This study aimed to examine the utility of histological features of WD in children compared to those with NAFLD and AIH. METHODS: A review of liver biopsy slides was performed in children with a clinical and/or genetic diagnosis of WD, seen at the Hospital for Sick Children between 1981 and 2019 and compared to controls with NAFLD and AIH. 37 children with WD and 37 disease controls (20 NAFLD; 17 AIH) were included. Three pathologists, blind to clinical details and diagnosis, reviewed all liver biopsies to reach consensus. Clinical and histopathologic features were compared between groups. RESULTS: Most WD cases displayed steatosis or steatohepatitis on histology (34/37), active AIH-pattern in 1 and inactive cirrhosis in 2 cases. Electron microscopy (EM) findings of mitochondrial abnormalities including dilated tips of cristae, pleomorphism, membrane duplication and dense matrix were more frequent in the WD group as compared to disease controls (p < 0.0001). In WD, dilated tips of mitochondrial cristae had a sensitivity of 91% and specificity of 86%, best among EM features. CONCLUSIONS: Light microscopic findings display considerable overlap among children with WD, NAFLD and AIH. Ultrastructural findings of mitochondrial abnormalities are important to distinguish WD from NAFLD and AIH. EM examination should be considered essential in the diagnostic work-up of paediatric liver biopsies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".