Post Living Donor Liver Transplantation Small-for-size Syndrome: Definitions, Timelines, Biochemical, and Clinical Factors for Diagnosis: Guidelines From the ILTS-iLDLT-LTSI Consensus Conference
Bibliographic record
Abstract
BACKGROUND: When a partial liver graft is unable to meet the demands of the recipient, a clinical phenomenon, small-for-size syndrome (SFSS), may ensue. Clear definition, diagnosis, and management are needed to optimize transplant outcomes. METHODS: A Consensus Scientific committee (106 members from 21 countries) performed an extensive literature review on specific aspects of SFSS, recommendations underwent blinded review by an independent panel, and discussion/voting on the recommendations occurred at the Consensus Conference. RESULTS: The ideal graft-to-recipient weight ratio of ≥0.8% (or graft volume standard liver volume ratio of ≥40%) is recommended. It is also recommended to measure portal pressure or portal blood flow during living donor liver transplantation and maintain a postreperfusion portal pressure of <15 mm Hg and/or portal blood flow of <250 mL/min/100 g graft weight to optimize outcomes. The typical time point to diagnose SFSS is the postoperative day 7 to facilitate treatment and intervention. An objective 3-grade stratification of severity for protocolized management of SFSS is proposed. CONCLUSIONS: The proposed grading system based on clinical and biochemical factors will help clinicians in the early identification of patients at risk of developing SFSS and institute timely therapeutic measures. The validity of this newly created grading system should be evaluated in future prospective studies.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".