Reassessing selection criteria prior to liver transplantation for hepatocellular carcinoma utilizing the scientific registry of transplant recipients database†‡
Bibliographic record
Abstract
The current model of liver graft allocation in place in the United States favors transplantation of patients with small hepatocellular carcinomas (HCCs) within the Milan criteria (a single tumor up to 5 cm in diameter or up to three lesions, none larger than 3 cm). Although several reports have suggested that these criteria could be extended, there is currently no agreement on new selection tools. In this study, we performed an overview of 6478 adult recipients of an isolated first liver transplant registered in the Scientific Registry of Transplant Recipients (SRTR) database. From March 2002 to January 2008, increasing numbers of patients outside Milan criteria (P ≤ 0.001) have been registered for a transplant, but they still represent less than 5% of the transplants performed for HCC. Of all the tested variables (tumor number, largest tumor size, and Milan and University of California San Francisco criteria), only total tumor volume (TTV; P ≤ 0.05) and alpha fetoprotein (AFP; P ≤ 0.001) could predict patient survival. While these two parameters demonstrated independent behaviors (no patient demonstrated an increase in both values), a composite score was defined, with patients with a TTV > 115 cm3 or an AFP > 400 ng/mL being outside criteria. The combined TTV/AFP score efficiently predicted posttransplant survival (hazard ratio = 2, 95% confidence interval = 1.7-2.4, P ≤ 0.001); patients not meeting these criteria had a survival below 50% at 3 years. Conclusion: According to the present SRTR data, Milan criteria are too restrictive, and patients with larger TTV can enjoy satisfactory posttransplant survivals. A composite patient selection score combining TTV and AFP was the most effective of all tested staging criteria for the prediction of posttransplant patient survival for candidates with HCC. (HEPATOLOGY 2009.)
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".