Early treatment response predicts the need for liver transplantation in autoimmune hepatitis
Bibliographic record
Abstract
Abstract: Background: The need for immunosuppression in autoimmune hepatitis is established. Previous studies have investigated short‐term outcomes in patients who respond to treatment. This study assesses long‐term prognosis of patients who fail to respond to standard immunosuppression. Methods: 163 charts were reviewed, composed of 108 non‐transplant patients and 55 patients who required liver transplantation (LT). Clinical endpoints were based on aminotransaminases: Early treatment response (ER) was a 50% improvement at 6 months of therapy, Complete remission (CR) was an improvement to <2X normal, Relapse was worsening to >3X normal, Incomplete response (IR) was some response but no CR in 3 years, and No response (NR) was no improvement after 3 years. Results: 85% of non‐LT and 25% of LT patients achieved ER, 91% of non‐LT and 26% of LT patients achieved CR. 41% of non‐LT patients relapsed on maintenance treatment, and 41% of non‐LT patients relapsed when withdrawn from treatment. 9% of non‐LT and 58% of LT patients had IR. 16% in LT group showed NR, while all non‐LT patients showed some response. All paired comparisons were statistically different (P<0.05). Multiple regression analysis revealed that lack of ER predicts need for LT (P=0.0005). 87% of patients who achieved ER did not require LT, whereas 16% of patients who failed ER showed NR and all required LT. Odds ratio of a patient who failed ER proceeding to LT, compared to a patient who achieved ER, was 16.8 (7.5 to 37.7, 95% CI). Conclusion: Patients who fail to show a 50% improvement in transaminases at 6 months of standard immunosuppression should be considered for alternate treatment modalities or be referred earlier for LT.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".