Utility of azathioprine metabolite measurements in post‐transplant recurrent autoimmune and immune‐mediated hepatitis
Bibliographic record
Abstract
Patients with post-transplant immune-mediated hepatitis (IMH) and recurrent autoimmune hepatitis (RAIH) have a poor outcome and a higher need for retransplantation. Azathioprine (AZA) is used as adjunctive immunosuppression after transplantation; optimizing its dose may be a key point in preserving graft function. Complications of high AZA dosing make dose escalation potentially problematic. Our aim was to correlate AZA metabolite levels with therapeutic effects, toxicity, and adherence to medication in children with IMH and RAIH. Charts of 14 patients were retrospectively reviewed. The post-transplant diagnosis was based on liver biopsy and autoimmune markers. AZA was prescribed after establishing the post-transplant diagnosis. AZA was started at 1.1 (1.0-1.8) mg/kg/day. Routine biochemical studies, tacrolimus levels, 6-thioguanine (6-TG) and 6-methylmercaptopurine levels were assessed every 8 wk. AZA dose was routinely adjusted to achieve 6-TG levels between 235 and 450 pmol per 8 x 10(8) RBC. A total of 92 samples from 14 patients were reviewed. Four patients were excluded because of non-adherence. AZA dose was increased by 245% resulting in eight of 10 patients in the target range; no hepatic or bone marrow toxicity was observed. ALT levels and steroid requirements were significantly reduced (p < 0.05). The AZA dose required to achieve target 6-TG levels was significantly greater in children <10 yr. AZA metabolite testing in children post-liver transplant is useful in assessing adherence to medication and it is potentially helpful in optimizing medication dosing. In younger children the AZA dose requirements were two to four times higher than previously reported standard doses.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".