New predictive factors of poor response to therapy in autoimmune hepatitis: role of mean platelet volume
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The response to immunosuppressive therapy in autoimmune hepatitis (AIH) is a matter of debate. The aim of this work is to identify the histological, biochemical, and clinical predictive factors of incomplete response/treatment failure to the standard treatment (prednisone with or without azathioprine) in a well-characterized series of AIH Egyptian patients. PATIENTS AND METHODS: Of 49 AIH patients, only 36 patients completed this retrospective cohort study. The immunological, biochemical, histopathological, and clinical characteristics of patients were evaluated at diagnosis and during follow-up. RESULTS: Patients were classified into two groups; group A showed a complete response to therapy (n=22; 61%) and group B showed partial response/treatment failure (n=14; 39%). In a multivariate analysis, we observed that age at diagnosis up to 22 years [odds ratio (OR): 23.22; confidence interval (CI): 3.978-135.549; P<0.001], serum albumin up to 3.2 g/dl (OR: 5.36; CI: 1.237-23.209; P=0.025), mean platelet volume (MPV) of at least 10.75 fl (OR: 16.5; CI: 3.093-88.037; P<0.001), and presence of cirrhosis at diagnosis (OR: 8.44; CI: 1.682-42.392; P=0.001) were independent variables that can predict partial response/treatment failure. MPV correlated positively with stages of fibrosis/cirrhosis and grades of activity in liver biopsy at diagnosis and correlated inversely with serum albumin and age at presentation. During therapy, group B showed a fluctuation in MPV levels, however, group A showed a progressive decline until the end point. CONCLUSION: Our study confirmed that younger age, hypoalbuminemia, increased MPV, and cirrhosis at diagnosis were all independent predictors of incomplete response/treatment failure in AIH patients. MPV may reflect the response to therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".