Liver transplantation for primary and secondary liver tumors: Patient-level meta-analyses compared to UNOS conventional indications
Bibliographic record
Abstract
BACKGROUND AND AIMS: Liver transplant (LT) for transplant oncology (TO) indications is being slowly adopted worldwide and has been recommended to be incorporated cautiously due to concerns about mid-long-term survival and its impact on the waiting list. APPROACH AND RESULTS: We conducted 4 systematic reviews of all series on TO indications (intrahepatic cholangiocarcinoma and perihilar cholangiocarcinoma [phCC]) and liver metastases from neuroendocrine tumors (NETs) and colorectal cancer (CRLM) and compared them using patient-level meta-analyses to data obtained from the United Network for Organ Sharing (UNOS) database considering conventional daily-practice indications. Secondary analyses were done for specific selection criteria (Mayo-like protocols for phCC, SECA-2 for CRLM, and Milan criteria for NET). A total of 112,014 LT were analyzed from 2005 to 2020 from the UNOS databases and compared with 345, 721, 494, and 103 patients obtained from meta-analyses on intrahepatic cholangiocarcinoma and phCC, and liver metastases from NET and CRLM, respectively. Five-year overall survival was 53.3%, 56.4%, 68.6%, and 53.8%, respectively. In Mantel-Cox one-to-one comparisons, survival of TO indications was superior to combined LT, second, and third LT and not statistically significantly different from LT in recipients >70 years and high BMI. CONCLUSIONS: Liver transplantation for TO indications has adequate 5-year survival rates, mostly when performed under the selection criteria available in the literature (Mayo-like protocols for phCC, SECA-2 for CRLM, and Milan for NET). Despite concerns about its impact on the waiting list, some other LT indications are being performed with lower survival rates. These oncological patients should be given the opportunity to have a definitive curative therapy within validated criteria.
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.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.042 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".