S3660 Liver Transplantation of a Foreign National With Acute Liver Failure Secondary to Chronic Hepatitis B Reactivation in Canada: A Case Report
Bibliographic record
Abstract
Introduction: Liver transplantation of foreign nationals (FNLT) who acutely decompensate remains to be an ethical dilemma that many Canadian transplant centres face. We present a case report of a cirrhotic foreign national who acutely decompensated in Alberta, Canada requiring liver transplantation. Case Description/Methods: A 57-year-old woman from Vietnam with chronic Hepatitis B (HBV) infection who was visiting family members in Alberta, Canada was admitted to hospital with liver failure secondary to reactivation of HBV. She was previously on tenofovir but was stopped due to financial reasons. She failed to respond to medical management including anti-viral therapies in-hospital and a decision was made to proceed with liver transplantation. She received a donation after cardiac death (DCD) liver with post-op complications including hepatic artery thrombosis (HAT) managed conservatively with heparin, and post-transplant diabetes managed with diabetic diet. The liver explant showed cirrhosis and severe hepatitis. Patient currently remains on a visitor visa in Canada, with plans for extending her stay and is paying out-of-pocket for all medications due to lack of insurance. Long-term plan includes life-long entecavir for HBV and aspirin for HAT, monthly intramuscular injections of hepatitis B immunoglobulin (HBIG) and mycophenolic acid for the first 12 months post-transplant and a generic form of tacrolimus. Discussion: Literature shows that many transplant programs in Canada will consider patients for transplant based on medical needs despite foreign nationality. The Canadian Liver Transplant Network has a consensus of accepting FNLT based on emergent need. Previously it permitted up to a maximum of 5% of the total available cadaveric livers. There remains challenges including lack of awareness among hepatologists of the existence of such consensus and obtaining financial coverage on a provincial level. This case report will hopefully generate a discussion and raise awareness on guidelines around FNLT in Canada.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".