Randomized trial investigating the utility of a liver tissue transcriptional biomarker in identifying adult liver transplant recipients not requiring maintenance immunosuppression
Bibliographic record
Abstract
The maintenance of stable allograft status in the absence of immunosuppression (IS), known as operational tolerance, can be achieved in a small proportion of liver transplant recipients, but we lack reliable tools to predict its spontaneous development. We conducted a prospective, multicenter, biomarker-strategy design, IS withdrawal clinical trial to determine the utility of a predictive biomarker of operational tolerance. The biomarker test, originally identified in a patient cohort with high operational tolerance prevalence, consisted of a 5-gene transcriptional signature measured in liver tissue collected before initiating IS weaning. One hundred sixteen adult stable liver transplant recipients were randomized 1:1 to either arm A (IS withdrawal regardless of biomarker status) or arm B (IS withdrawal in biomarker-positive recipients). Immunosuppression withdrawal was initiated in 82 participants, rejection occurred in 54 (67.5%), and successful discontinuation of IS was achieved in 22 (27.5%), but only 13 (16.3%) met operational tolerance histologic criteria (10 in arm A; 3 in arm B). The biomarker test did not yield useful information in selecting patients able to successfully discontinue IS. Operational tolerance was associated with time posttransplant, recipient age, presence of circulating exhausted CD8 + T cells, and a reduced number of immune synapses within the graft.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".