The role of living donor liver transplantation in colorectal cancer liver metastases
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite technical and therapeutic advances, only 20-40% of patients with colorectal liver metastases (CRLM) have resectable disease. Historically, the remaining patients with unresectable, liver-only CRLM would receive palliative chemotherapy, with a median survival of 8 months. RECENT FINDINGS: Liver transplantation has emerged as a viable option for selected patients with CRLM. This advancement stems from improved understanding of tumour genomics and biology and better patient selection criteria. The results of recent prospective clinical trials have further ignited enthusiasm for liver transplantation as a viable therapeutic option. Living donor liver transplantation (LDLT) offers several advantages over deceased donor liver transplantation (DDLT) for this disease, including reduced wait-time and optimized timing and coordination of oncologic therapy. On-going LDLT clinical trials have demonstrated favourable outcomes as compared with other liver transplantation indications. However, there is no established consensus or standardization in the implementation of LDLT for CRLM, beyond trials and centre-specific protocols. SUMMARY: LDLT is an excellent therapeutic option in highly selected patients with CRLM. Refining prognostic factors and selection criteria will help to further optimize the utility and broaden the acceptance and implementation of LDLT for patients with CRLM.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".