Anesthesia and Critical Care for the Prediction and Prevention for Small-for-size Syndrome: Guidelines from the ILTS-iLDLT-LTSI Consensus Conference
Bibliographic record
Abstract
BACKGROUND: During the perioperative period of living donor liver transplantation, anesthesiologists and intensivists may encounter patients in receipt of small grafts that puts them at risk of developing small for size syndrome (SFSS). METHODS: A scientific committee (106 members from 21 countries) performed an extensive literature review on aspects of SFSS with proposed recommendations. Recommendations underwent a blinded review by an independent expert panel and discussion/voting on the recommendations occurred at a consensus conference organized by the International Liver Transplantation Society, International Living Donor Liver Transplantation Group, and Liver Transplantation Society of India. RESULTS: It was determined that centers with experience in living donor liver transplantation should utilize potential small for size grafts. Higher risk recipients with sarcopenia, cardiopulmonary, and renal dysfunction should receive small for size grafts with caution. In the intraoperative phase, a restrictive fluid strategy should be considered along with routine use of cardiac output monitoring, as well as use of pharmacologic portal flow modulation when appropriate. Postoperatively, these patients can be considered for enhanced recovery and should receive proactive monitoring for SFSS, nutrition optimization, infection prevention, and consideration for early renal replacement therapy for avoidance of graft congestion. CONCLUSIONS: Our recommendations provide a framework for the optimal anesthetic and critical care management in the perioperative period for patients with grafts that put them at risk of developing SFSS. There is a significant limitation in the level of evidence for most recommendations. This statement aims to provide guidance for future research in the perioperative management of SFSS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".