Natural History and Treatment Outcomes of Severe Autoimmune Hepatitis
Bibliographic record
Abstract
GOALS: The aim of this study was to analyze the natural history and treatment outcomes of autoimmune hepatitis (AIH) variants presenting with severe-AIH. BACKGROUND: Severe acute presentation is an uncommon manifestation of AIH, and it remains poorly characterized. MATERIALS AND METHODS: We included 101 patients with AIH from January 2011 to December 2015. Patients were classified as seropositive-AIH and seronegative-AIH. Patients with acute liver failure, acute-on-chronic liver failure, and severe acute hepatitis were defined as severe-AIH patients. Patient characteristics and treatment outcomes with follow-up until 12 months were analyzed between the different groups. RESULTS: Out of 101 cases, 24 (23.76%) had severe AIH. Of them 9 (37.5%) had severe acute hepatitis, 3 (12.5%) had acute liver failure, and 12 (50%) had acute-on-chronic liver failure. Seronegative-AIH patients presented with severe-AIH significantly more frequently compared with seropositive-AIH patients (50% vs. 20.27%, P=0.022). Severe-AIH had 50% complete responders, 25% partial responders, and 25% treatment failures. Jaundice (88.88% vs. 68.7%, P=0.048), encephalopathy (55.55% vs. 6.66%, P=0.014), and higher international normalized ratio values (2.17±0.60 vs. 1.82±0.14, P=0.038) were factors associated with nonresponse rather than the presence or absence of autoantibodies in severe-AIH. The hazard ratio for predicting remission in the non-severe AIH group as compared with the severe-AIH group was 1.502, which was statistically not significant (95% CI, 0.799-2.827; P=0.205). CONCLUSION: Approximately 24% of patients with AIH have severe-AIH. Conventional autoantibodies are often absent in severe-AIH; however, it does not alter the outcome. Immunosuppressants should be given expediently in patients with severe-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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".