Median Meld at Transplant Minus 3 Reduces the Mortality of Non-Hepatocellular Carcinoma Patients on the Liver Transplant Waitlist
Bibliographic record
Abstract
Liver transplants (LTs) are prioritized by mortality risk, which is estimated by MELD scores. Since hepatocellular carcinoma (HCC) patients present with lower MELD scores, they are allocated MELD exception points. Concerns persist that HCC recipients are over-prioritized, resulting in disproportionate waitlist mortality among non-HCC patients. We assessed whether the Median Meld at Transplant minus 3 (MMaT-3) scoring system would balance waitlist mortality and transplantation rates between HCC and non-HCC patients. We reviewed 266 patient charts listed for an LT from 2015 to 2023; 46.2% were listed in the MMaT-3 era. Amongst non-HCC patients, MMaT-3 implementation significantly increased 1-year transplant rate and reduced 1-year waitlist mortality among non-HCC patients (p = 0.003). Pre-MMaT-3 gaps in transplantation (p = 0.004) and waitlist dropout (p = 0.01) were eliminated post-implementation (p > 0.05). Amongst HCC patients, MMaT-3 implementation had no impact on the 1-year transplant rate (p = 0.92) or 1-year waitlist mortality (p = 0.66). Fine-gray proportional hazard multivariable analysis revealed that MMaT-3 significantly reduced waitlist mortality among non-HCC patients (asHR: 0.44, 95% CI [0.23, 0.83], p = 0.01) and limited impact on HCC patients (p = 0.31). MMaT-3 allocation did not significantly alter 2-year post-transplant survival for both populations. We show that the MMaT-3 system decreased the waitlist mortality of non-HCC patients with limited impacts on outcomes for HCC patients listed for an LT.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".