A218 THE RISK OF RECURRENT HEPATOCELLULAR CARDINOMA IN POST-LIVER TRANSPLANT PATIENTS RECEIVING CAPECITABINE TREATMENT
Bibliographic record
Abstract
Abstract Background Little is known on how to reduce the risk of hepatocellular carcinoma (HCC) recurrence post liver transplantation (LT). We examined if adjuvant oral Capecitabine reduces the risk of recurrent HCC in a high-risk group post-LT. Aims To examine if adjuvant oral Capecitabine reduces the risk of recurrent HCC in a high-risk group post-LT. Methods A retrospective study was performed from a pre-existing liver transplant database from the Liver Transplant Unit at London Health Sciences Center, London; Canada. This database contains demographic, clinical parameters and follow-up of all patients transplanted for HCC. Data was extracted for patients who underwent LT between January 2000 – April 2018 and included follow up until May 31st, 2020. High-risk of tumor recurrence was defined as a RETREAT score ≥5 or PARFITT score ≥10.5. Log rank test compared the recurrence of HCC or death among patients who were and were not prescribed Capecitabine. Results Out of 168 LT for HCC, 25 patients were identified as high-risk group for recurrence. The median age was 63 years (IQR=60–65). 19 (76%) patients had viral hepatitis including Hepatitis B and Hepatitis C as their primary disease while 4 (16%) patients had NASH. The remaining 2 (8%) patients had Autoimmune Hepatitis. 7 (28%) patients received Capecitabine while 18 (72%) did not. All patients were followed for a median of 22 months (IQR=8.9–57.5). No statistical significance difference was found between the two groups with respect to HCC recurrence or death (p=0.56). Conclusions Among patients with high risk features for recurrence of HCC, adding Capecitabine therapy added to conventional immunosuppression had no overall effect on reducing overall tumor recurrence or survival. Funding Agencies None
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".