Total tumor volume predicts risk of recurrence following liver transplantation in patients with hepatocellular carcinoma
Why this work is in the frame
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Bibliographic record
Abstract
Criteria for the selection of candidates for liver transplantation in the presence of hepatocellular carcinoma (HCC) should accurately predict posttransplant recurrence while not excluding excessive numbers of patients from candidacy. Existing criteria are challenged by the limited accuracy of radiological assessment. The total tumor volume (TTV) was calculated by the addition of the volume of each individual tumor. A preliminary analysis was carried out on HCC patient data from the Alberta Liver Transplant Program (52 patients) and then validated on the populations of the Universities of Toronto and Colorado programs (154 and 82 patients). A TTV cutoff of 115 cm(3) was chosen on the basis of the risk of recurrence with use of a receiver operating characteristic curve. Radiology correlated more closely to pathology with TTV than with Milan and University of California at San Francisco (UCSF) criteria (91% versus 69% and 75% of patients, P < 0.0001). Although more patients met qualifying criteria for transplant with TTV (28%-53% more than Milan and 16%-26% more than UCSF), no deterioration of outcome was demonstrated in an analysis of patients within TTV < or = 115 cm(3) in comparison with those meeting Milan or UCSF classifications at all institutions. Patients with TTV > 115 cm(3) experienced more recurrences and lower patient survival in the Alberta and Colorado series (P < 0.05). When TTV with a cutoff of 115 cm(3) is used for candidate selection, the accuracy of pretransplant radiological assessment is enhanced, with posttransplant outcomes not different from those achieved with Milan and UCSF classifications despite a more inclusive patient population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it