Bibliographic record
Abstract
BACKGROUND: Autoimmune hepatitis has diverse clinical phenotypes and outcomes that challenge current diagnostic criteria and management algorithms. OBJECTIVES: To highlight the major difficulties in diagnosis and management, describe the efforts to ease them and encourage further progress in problem solving. METHODS: The MEDLINE database was reviewed for published experiences from 1984 to 2013. RESULTS: Acute or acute severe (fulminant) hepatitis, asymptomatic mild disease, and histological findings of centrilobular necrosis or bile duct injury can confound diagnosis and treatment. Continuation of conventional therapy until normal liver test results and liver tissue reduces the frequency of relapse, but does not prevent its occurrence. Problematic patients can be identified using mathematical models, clinical phenotype, serological markers and the speed of improvement after treatment; however, their recognition and treatment are inconsistent. Mycophenolate mofetil can rescue patients with azathioprine intolerance but is less effective for refractory disease. Budesonide in combination with azathioprine can be used frontline, but is effective primarily in noncirrhotic, uncomplicated disease. Molecular and cellular interventions are feasible but largely unevaluated. DISCUSSION: Resolution of the current challenges requires revision of diagnostic criteria, characterization of biological markers that reflect pathogenic pathways, development of dynamic indexes based on changes in disease behaviour, and introduction of new pharmacological, molecular and cellular interventions that have undergone rigorous evaluation. CONCLUSION: These challenges reflect important remediable deficiencies in current management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".