A prospective multicenter validation of RETREAT for posttransplantation HCC recurrence prediction
Bibliographic record
Abstract
BACKGROUND AND AIMS: The RETREAT(Risk Estimation of Tumor REcurrence After Transplant) score is a simple risk stratification tool for postliver transplantation (LT) HCC recurrence that has been validated in retrospective cohort studies. A prospective, multicenter study is needed to further demonstrate accuracy especially given the evolving clinical demographics and HCC transplant practice. Our aim is to validate and compare the RETREAT score to other post-LT HCC recurrence risk scores in a contemporary, prospective cohort of patients. APPROACH AND RESULTS: We prospectively enrolled patients with HCC who underwent LT from 8 centers between 2018 and 2022. The primary outcome was post-LT recurrence-free survival. Secondary outcomes included post-LT and post-recurrence survival. Model performance, determined using the concordance index, Akaike information criterion, integrated Brier score, and calibration, was compared to that of other established risk scores.We included 1166 patients with HCC who underwent LT, of which 78 (6.7%) had post-LT HCC recurrence after a median follow-up time of 2.2 years (IQR 1.2-3.2). The median RETREAT score was 4 (IQR 3-5) in patients with post-LT HCC recurrence and 1 (IQR 1 - 2) in patients without. Those with a RETREAT score of 0, 3, and 5+ had a 99.4%, 84.1%, and 55.6% recurrence-free survival, respectively, at 3 years post-LT. The RETREAT score was also able to stratify post-LT overall and postrecurrence survival. The RETREAT score's concordance index was 0.81 (95% CI: 0.77-0.85) and outperformed the MORAL and RELAPSE scores across multiple metrics. CONCLUSIONS: The RETREAT score retains high accuracy for predicting post-LT HCC recurrence, further supporting RETREAT-guided post-LT HCC surveillance and care.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".