When donor T cells attack: The curious case of liver transplant-associated acute graft-versus-host-disease
Bibliographic record
Abstract
Graft versus host disease is a rare but deadly complication of solid organ transplant. Clinical features of graft-versus-host-disease are non-specific, which may lead to delayed diagnosis as more common conditions including infections or drug reactions are considered. We describe a 54-year-old male patient who underwent liver transplantation for alcohol use disorder-related cirrhosis and developed acute graft-versus-host disease. Initial clinical presentation included dermatitis, bone marrow failure and enteritis. Results of skin biopsy and cytogenetic studies were consistent with liver transplant-associated acute graft-versus-host disease. The importance of this case is to highlight to transplant physicians and surgeons the challenges of diagnosing graft-versus-host-disease. In our case, pre-existing partnerships among the liver and hematopoietic stem cell transplant teams, transfusion medicine specialists, critical care specialists and facilitated timely communication relevant to confirming graft-versus-host disease. We propose an algorithm to assist in the workup of suspected graft-versus-host disease. Because this condition is characterized by high mortality, a high index of suspicion is imperative for prompt diagnosis and optimal management of the donor-recipient immune interaction when patients present with classic clinical features.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".