How far can we go with hepatocellular carcinoma in living donor liver transplantation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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