Small‐for‐size syndrome in live donor liver transplantation—Pathways of injury and therapeutic strategies
Bibliographic record
Abstract
Due to the severe organ shortage and the increasing gap between the supply and demand for donor grafts, live donor liver transplantation (LDLT) has become an accepted and alternative technique for the expansion of the donor pool. However, donor safety and good recipient outcomes must be balanced regarding risk stratification and decision-making within this patient population. Small-for-size syndrome (SFSS) is one of the complications encountered after LDLT, thus increasing the burden of optimizing donor graft selection and effective treatments during its occurrence. A graft-to-recipient weight ratio (GRWR) <0.8 predisposes the graft to SFSS. However, other factors may induce this complication even without a graft-to-patient size mismatch. Several strategies to prevent this complication include portal vein flow and liver outflow modulation, as well as pharmacological treatment. Also, as an entity with a multifactorial etiology, outcomes vary between right-lobe, left-lobe, and posterior-lobe donation among series encountered in the literature. In this review, we analyze the pathophysiology and classification of this complication, the state-of-the-art on management of SFSS, and the outcomes regarding the best treatment strategy on this patient population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".