Liver Transplantation Without Venovenous Bypass: Does Surgical Approach Matter?
Bibliographic record
Abstract
BACKGROUND: The use of venovenous bypass in liver transplantation has declined over time. Few studies have examined the impact of surgical approach in cases performed exclusively without venovenous bypass. We hypothesized that advances in liver transplant anesthesia and perioperative care have minimized the importance of surgical approach in the modern era. METHODS: Deceased donor liver transplants at the University of Toronto from 2000 to 2015 were reviewed, all performed without venovenous bypass. First, an unadjusted analysis was performed comparing perioperative outcomes and graft/patient survival for 3 different liver transplant techniques (caval interposition, piggyback, side-to-side cavo-cavostomy). Second, a propensity-matched analysis was performed comparing caval interposition to caval-preserving techniques. RESULTS: One thousand two hundred thirty-three liver transplants were included in the study. On unadjusted analysis, blood loss, transfusion requirement, postoperative complications, and graft/patient survival were equivalent for the 3 different techniques. To account for possible confounding patient variables, propensity matching was performed. Analysis of the propensity-matched cohorts also demonstrated similar outcomes for caval interposition versus caval-preserving approaches. CONCLUSIONS: In the modern era at centers with a multidisciplinary team, the importance of specific liver transplant technique is minimized. Full or partial cross-clamping of the inferior vena cava is feasible without the use of venovenous bypass.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".