The extended Toronto criteria for liver transplantation in patients with hepatocellular carcinoma: A prospective validation study
Bibliographic record
Abstract
The selection of liver transplant candidates with hepatocellular carcinoma (HCC) relies mostly on tumor size and number. Instead of relying on these factors, we used poor tumor differentiation and cancer-related symptoms to exclude patients likely to have advanced HCC with aggressive biology. We initially reported similar 5-year survival for patients whose tumors exceeded (M+ group) and were within (M group) the Milan criteria. Herein, we validate our original data with a new prospective cohort and report the long-term follow-up (10-years) using an intention-to-treat analysis. The previously published study (cohort 1) included 362 listed (294 transplanted) patients from January 1996 to August 2008. The validation cohort (cohort 2) includes 243 listed (105 M+ group, 76 beyond University of California San Francisco criteria; 210 transplanted) patients from September 2008 to December 2012. Median follow-up from listing was 59.7 (26.8-103) months. For the validation cohort 2, the actuarial survival from transplant for the M+ group was similar to that of the M group at 1 year, 3 years, and 5 years: 94%, 76%, and 69% versus 95%, 82%, and 78% (P = 0.3). For the combined cohorts 1 and 2, there were no significant differences in the 10-year actuarial survival from transplant between groups. On an intention-to-treat basis, the dropout rate was higher in the M+ group and the 5-year and 10-year survival rates from listing were decreased in the M+ group. An alpha-fetoprotein level >500 ng/mL predicted poorer outcomes for both the M and M+ groups. CONCLUSION: Tumor differentiation and cancer-related symptoms of HCC can be used to select patients with advanced HCC who are appropriate candidates for liver transplantation; alpha-fetoprotein level limitations should be incorporated in the listing criteria for patients within or beyond the Milan criteria. (Hepatology 2016;64:2077-2088).
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".