Choice of calcineurin inhibitor may influence the development of <i>de novo</i> immune hepatitis associated with anti‐GSTT1 antibodies after liver transplantation
Bibliographic record
Abstract
Aguilera I, Sousa JM, Praena JM, Gómez‐Bravo MA, Núñez‐Roldan A. Choice of calcineurin inhibitor may influence the development of de novo immune hepatitis associated with anti‐GSTT1 antibodies after liver transplantation. Clin Transplant 2011: 25: 207–212. © 2010 John Wiley & Sons A/S. Abstract: In 2004, we defined the genetic mismatch in the glutathione S‐transferase T1 (GSTT1) gene positive donor/null recipient as a risk factor to develop de novo immune hepatitis (IH) after liver transplant (LT), which is always associated with production of donor‐specific anti‐GSTT1 antibodies. However, there are several unresolved questions, such as why some of these patients produce antibodies, why others do not and why not all of the patients with antibodies develop the disease. The aim of this study was to evaluate the influence of several variables in the production of anti‐GSTT1 antibodies and/or de novo IH. The study group included 35 liver‐transplanted patients. The number of patients not producing antibodies was significantly higher in the group treated with Tac‐based immunosuppression compared with the CsA‐based group (94.1% vs. 5.9%, p = 0.001). Additionally, a protective effect of the Tac‐based therapy vs. the CsA‐based therapy was observed with regard to development of de novo IH (80.8% vs. 19.2%, p = 0.003). In conclusion, the choice of calcineurin inhibitor may influence the development of de novo IH mediated by anti‐GSTT1 antibodies.
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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 | 0.000 |
| 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".