Hepatocellular carcinoma and survival in patients with autoimmune hepatitis (<scp>J</scp>apanese <scp>N</scp>ational <scp>H</scp>ospital <scp>O</scp>rganization‐autoimmune hepatitis prospective study)
Bibliographic record
Abstract
BACKGROUND/AIMS: Although the outcome of autoimmune hepatitis (AIH) is generally good, the natural course and likelihood of progression to cirrhosis or hepatocellular carcinoma (HCC) remain undefined, and may vary by region and population structure. Our aims were to evaluate risk factors that contribute to poor outcome and particularly development of HCC in a prospective multicentric cohort study of AIH. METHODS: The study group comprised 193 Japanese patients with AIH who were prospectively followed up at annual intervals between 1995 and 2008. The mean follow-up period was 8.0 ± 4.5 years. RESULTS: Twenty-one (10.9%) patients had cirrhosis at presentation and a further 15 (7.8%) developed cirrhosis during the follow-up period. Survival rates were 94.2% at 10 years and 89.3% at 15 years. HCC was diagnosed in seven of the 193 patients. The presence of cirrhosis at presentation was a risk factor for HCC according to a Cox proportional hazard model, and the HCC-free survival rate was significantly lower in those with cirrhosis compared to those without cirrhosis according to Kaplan-Meier analysis. CONCLUSIONS: Although the outcome of AIH is as good if not better among Japanese than for other populations, there was an increased risk of HCC in these patients. Cirrhosis at presentation was predictive of development of HCC in AIH in Japan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".