Should We Exclude Live Donor Liver Transplantation for Liver Transplant Recipients Requiring Mechanical Ventilation and Intensive Care Unit Care?
Bibliographic record
Abstract
UNLABELLED: Patients with acute and chronic liver disease often require admission to intensive care unit (ICU) and mechanical ventilation support before liver transplantation (LT). Rapid disease progression and high mortality on LT waiting lists makes live donor LT (LDLT) an attractive option for this patient population. METHODS: During 2000 to 2011, all ICU-bound and mechanically ventilated patients receiving an LDLT (n = 7) were compared to patients receiving a deceased donor LT (DDLT) (n = 38). RESULTS: Both groups were comparable regarding length of pretransplant ICU stay (DDLT: 2 [1-31] days vs LDLT: 2 [1-8] days; P = 0.2), days under mechanical ventilation (DDLT: 2 [1-31] days vs LDLT: 2 [1-5] days; P = 0.2), pretransplant dialysis (DDLT: 45% vs LDLT: 43%; P = 1) and model for end-stage liver disease score (DDLT: 33 ± 8 vs LDLT: 33 ± 10; P = 0.911). Live donors median evaluation time was 24 hours (18-561 hours). As expected, median time on waiting list was significantly lower in the LDLT group (DDLT: 13 [0-1704] days vs LDLT: 10 [1-33] days; P = 0.008). Incidence of postoperative complications was numerically, albeit not significantly higher in the DDLT versus LDLT (68% vs 29%; P = 0.08). No difference was detected between LDLT and DDLT patients regarding 1-year (DDLT: 76% vs LDLT: 85%), 3-year (DDLT: 68% vs LDLT: 85%), and 5-year (DDLT: 68% vs LDLT: 85%) graft and patient survivals (P = 0.41). No severe donor complication occurred after live donation. CONCLUSIONS: The LDLT may provide a faster access to transplantation and therefore, offers an alternative treatment option for critically ill patients requiring ICU care and mechanical ventilation support at the time of transplantation.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".