Management of Established Small-for-size Syndrome in Post Living Donor Liver Transplantation: Medical, Radiological, and Surgical Interventions: Guidelines From the ILTS-iLDLT-LTSI Consensus Conference
Bibliographic record
Abstract
Small-for-size syndrome (SFSS) following living donor liver transplantation is a complication that can lead to devastating outcomes such as prolonged poor graft function and possibly graft loss. Because of the concern about the syndrome, some transplants of mismatched grafts may not be performed. Portal hyperperfusion of a small graft and hyperdynamic splanchnic circulation are recognized as main pathogenic factors for the syndrome. Management of established SFSS is guided by the severity of the presentation with the initial focus on pharmacological therapy to modulate portal flow and provide supportive care to the patient with the goal of facilitating graft regeneration and recovery. When medical management fails or condition progresses with impending dysfunction or even liver failure, interventional radiology (IR) and/or surgical interventions to reduce portal overperfusion should be considered. Although most patients have good outcomes with medical, IR, and/or surgical management that allow graft regeneration, the risk of graft loss increases dramatically in the setting of bilirubin >10 mg/dL and INR>1.6 on postoperative day 7 or isolated bilirubin >20 mg/dL on postoperative day 14. Retransplantation should be considered based on the overall clinical situation and the above postoperative laboratory parameters. The following recommendations focus on medical and IR/surgical management of SFSS as well as considerations and timing of retransplantation when other therapies fail.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".