Trajectories of patients relisted for liver transplantation
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Recurrent cirrhosis complicates 10-30% of Liver transplants (LT) and can lead to consideration for re-transplantation. We evaluated the trajectories of relisted versus primary listed patients on the waitlist using a competing risk framework. MATERIALS AND METHODS: We retrospectively examined 1,912 patients listed for LT at our centre between from 2012 to 2020. Cox proportional hazard models were used to assess overall survival (OS) by listing type and competing risk analysis Fine-Gray models were used to assess cumulative incidence of transplant by listing type. RESULTS: 1,731 patients were included (104 relisted). 44.2% of relisted patients received exception points vs. 19.8% of primary listed patients (p<0.001). Patients relisted without exceptions, representing those with graft cirrhosis, had the worst OS (HR: 4.17, 95%CI 2.63 - 6.67, p=<0.0001) and lowest instantaneous rate of transplant (HR: 0.56, 95%CI 0.38 - 0.83, p=0.006) than primary listed with exception points. On multivariate analysis listing type, height, bilirubin and INR were associated with cumulative incidence of transplant, while listing type, bilirubin, INR, sodium, creatinine were associated with OS. Within relisted patients, there was a trend towards higher mortality (HR: 1.79, 95%CI 0.91 - 3.52, p=0.08) and low transplant incidence (HR: 0.51, 95%CI 0.22 - 1.15, p=0.07) for graft cirrhosis vs other relisting indications. CONCLUSIONS: Patients relisted for LT are carefully curated and comprise a minority of the waitlist population. Despite their younger age, they have worse liver/kidney function, poor waitlist survival, and decreased transplant incidence suggesting the need for early relisting, while considering standardized exception points.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".