From Metro Ticket to a Cape-to-Khartoum Pass: Reimagining Liver Transplantation Criteria for HCC in Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Current liver transplantation (LT) criteria and the Metroticket model were developed in high-income countries (HICs) and may not be appropriate for hepatocellular carcinoma (HCC) patients in sub-Saharan Africa (SSA). METHODS: A retrospective observational cohort study of 647 HCC patients managed at Groote Schuur Hospital, Cape Town, South Africa was performed. Our LT experience and outcomes were reviewed, and current transplant criteria (Milan, University of California San Franciso, extended Toronto) were applied to non-transplanted patients. An exploratory cohort was then generated and analysed to inform a regionally adapted LT framework for SSA. RESULTS: Six (0.9%) patients underwent LT with a median survival of 2557.5 (range:1049-3076) days. In the non-transplanted group, 31(4.8%) met at least one LT criterion, of whom 19 (61.3%) were eventually treated with resection (12) and ablation (7). The exploratory cohort (n = 140) was predominantly male (84.2%) with advanced HCC (BCLC stage C/D in 79.2%), but preserved liver function (median MELD-Na of 10 and Child-Turcotte-Pugh grade A disease in 61.4%). Only 16.2% of the exploratory cohort received curative-intended therapies. Patients who underwent LT survived significantly longer than the two other patient groups (P < .001). CONCLUSION: This study highlights the potential limited applicability of current LT criteria in SSA, where applying criteria, less than 5% of HCC patients qualify. We propose a novel Cape-to-Khartoum framework incorporating clinical and biological parameters, including tumour markers, tumour differentiation and multi-omic profiling. This model may broaden LT eligibility and improve outcomes for HCC in SSA and warrants further validation through multicentre studies across the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".