Comparing Efficacy Between Regimens in the Initial Treatment of Autoimmune Hepatitis
Bibliographic record
Abstract
BACKGROUND: Autoimmune hepatitis is a chronic syndrome characterized by auto immunologic features generally including the presence of circulating auto antibodies and high serum globulin concentrations. The American Association for the Study of Liver Diseases (AASLD) recommends initial treatment or induction therapy for autoimmune hepatitis to involve a glucocorticoid alone or a combination of a glucocorticoid and an immunosuppressant. The objective of this study is to review and compare the efficacy of the treatment regimens described above among patients diagnosed with and treated for autoimmune hepatitis over the past 10 years in our center which is a major university based hospital. METHODS: We retrospectively identified patients above the age of 18 years diagnosed with autoimmune hepatitis in our center between February, 2003 and February, 2013 using the ICD-9 code 571.42. The primary outcome of our study was efficacy of the treatment regimen. We defined efficacy by considering 3 scenarios: Complete Resolution, Incomplete Resolution and Treatment Failure. RESULTS: We found differences among 3 treatment groups: patients who received Prednisone and immunosuppressant from the beginning of their treatment course, patients who had an immunosuppressant introduced after about 4 weeks on Prednisone and patients who were placed on Prednisone alone. CONCLUSION: From our study, better efficacy was achieved in the induction phase using a combination of Prednisone and Azathioprine from the beginning of the treatment course.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".