Living liver donor safety: Intraoperative considerations in living liver donation—Guidelines from the ILTS–iLDLT group consensus conference 2025
Bibliographic record
Abstract
The success of living donor liver transplantation (LDLT) has bridged the growing gap between organ demand and availability in patients with liver failure or liver cancer. Safety of the living liver donor (LLD) is paramount, and astute intraoperative management plays a critical role in ensuring optimal outcomes for them. Given that intraoperative surgical and anesthesia practices vary widely across centers, the International Liver Transplantation Society (ILTS) and the International Living Donor Liver Transplantation Group (iLDLT Group) convened a global consensus conference in Toronto in March 2025 to develop guidelines for intraoperative management of the LLD. This manuscript details the recommendations on intraoperative considerations for the safety of LLD developed by Working Group II of the Consensus Scientific Committee, using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system for assessment of recommendations, and the Danish model of consensus. Working Group II focused on intraoperative monitoring, fluid and ventilation strategies, surgical techniques for vascular and biliary division, surgeon and center-specific experience, and intraoperative crises planning. These guidelines aim to support both established and emerging LDLT teams to ensure high-quality and safe intraoperative care for the LLD.
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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 | 0.066 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".