Long-Term Outcomes of Recipients of Liver Transplants from Living Donors Treated with a Very Low-Calorie Diet
Bibliographic record
Abstract
The increasing prevalence of steatotic liver disease (SLD) in potential living donors is concerning, as it limits donor’s availability amid rising demand. OPTIFAST very low-calorie diet (VLCD), a meal replacement product, effectively reduces weight and hepatic steatosis before transplantation. However, data on the outcomes of recipients of VLCD-treated donors are lacking. We conducted a single-center, retrospective study on 199 living donor liver transplant recipients at Toronto General Hospital, Canada, between January 2015 and January 2020. We compared the 1-year posttransplant outcomes between recipients who received organs from donors treated with VLCD (N = 34) for either weight loss or steatosis reduction, with those who did not require treatment (N = 165). Our analysis revealed no statistically significant differences in the rates of postoperative complications (23% vs 32.4%, p=0.3 ) or intensive care unit stays (70.9% vs 70.6%, p=1 ) between recipients of non-VLCD and VLCD grafts. Following adjusted multivariate logistic regression, receipt of VLCD grafts was not associated with increased hospital length of stay. In addition, one-year mortality did not differ between the two groups (4.2% non-VLCD recipients vs 2.9% VLCD recipients, p=0.6 ). OPTIFAST VLCD treatment for liver donors demonstrates positive and safe outcomes in recipients, expanding the pool of potential living donors for increased organ availability.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".