Early Intervention With Live Donor Liver Transplantation Reduces Resource Utilization in NASH: The Toronto Experience
Bibliographic record
Abstract
BACKGROUND: In parallel with the obesity epidemic, liver transplantation for nonalcoholic steatohepatitis (NASH) is increasing dramatically in North America. Although survival outcomes are similar to other etiologies, liver transplantation in the NASH population has been associated with significantly increased resource utilization. We sought to compare outcomes between live donor liver transplantation (LDLT) and deceased donor liver transplantation (DDLT) at a high volume North American transplant center, with a particular focus on resource utilization. METHODS: The study population consists of primary liver transplants performed for NASH at Toronto General Hospital from 2000 to 2014. Recipient characteristics, perioperative outcomes, graft and patient survivals, and resource utilization were compared for LDLT versus DDLT. RESULTS: A total of 176 patients were included in the study (48 LDLT vs 128 DDLT). LDLT recipients had a lower model for end-stage liver disease score and were less frequently hospitalized prior to transplant. Estimated blood loss and early markers of graft injury were lower for LDLT. LDLT recipients had a significantly shorter hospitalization (intensive care unit, postoperative, and total hospitalization). CONCLUSIONS: LDLT for NASH facilitates transplantation of patients at a less severe stage of disease, which appears to promote a faster postoperative recovery with less resource utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".