Liver transplantation for hepatocellular carcinoma: pre-transplant considerations and post-transplant management
Bibliographic record
Abstract
Hepatocellular cancer is the seventh most frequent and third leading cause of cancer death worldwide. At its early stages, it could be amenable to complete cure using surgical resection or ablative therapies. Unfortunately, some patients present with advanced tumor stages or have reduced liver function and locoregional therapies may not be used or effective. Liver transplantation becomes an appealing option as it replaces the ailing liver and offers a chance for complete cure. Outcomes of liver transplantation for hepatocellular cancer have improved with better patient selection and adjuvant therapies allow patients to become eligible for transplantation and minimize dropouts from the waiting list. Post-transplant care, including appropriate surveillance and immunosuppression can also improve long-term outcomes with survival similar to transplantation for non-oncologic indications.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Clinical review of liver transplantation for hepatocellular carcinoma.
This is a clinical review of liver transplantation for hepatocellular carcinoma, not a review of research methods.
Clinical review of liver transplantation for hepatocellular carcinoma management.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".