How far can we go with hepatocellular carcinoma in living donor liver transplantation?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Living donor liver transplantation (LDLT) in the setting of hepatocellular carcinoma (HCC) has been adopted worldwide over the past decade. Many centers have implemented LDLT because of the limited supply of deceased organs, which has also provided an opportunity for centers to expand the indication for transplantation for patients with HCC. RECENT FINDINGS: Center-specific expanded HCC criteria have proven to be well tolerated in terms of overall and disease-free survival when compared with the standard, Milan criteria. There is a need to overcome size and number as the sole limiters. New technologies to better predict outcomes after liver transplantation for HCC, response to treatments and/or bridging therapies while waiting for a liver transplantation, along with determining tumour behaviour are being incorporated into criteria. Improved outcomes of LDLT for all causes has increased utilization of the procedure for HCC patients worldwide. SUMMARY: LDLT has become a great treatment option for HCC patients. Progressively better understanding of tumour behaviour and different surrogates of tumour biology assessments will allow better patient selection for LDLT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".