Frailty in liver transplantation: An expert opinion statement from the American Society of Transplantation Liver and Intestinal Community of Practice
Bibliographic record
Abstract
Frailty has emerged as a powerful predictor of outcomes in patients with cirrhosis and has inevitably made its way into decision making within liver transplantation. In an effort to harmonize integration of the concept of frailty among transplant centers, the AST and ASTS supported the efforts of our working group to develop this statement from experts in the field. Frailty is a multidimensional construct that represents the end-manifestation of derangements of multiple physiologic systems leading to decreased physiologic reserve and increased vulnerability to health stressors. In hepatology/liver transplantation, investigation of frailty has largely focused on physical frailty, which subsumes the concepts of functional performance, functional capacity, and disability. There was consensus that every liver transplant candidate should be assessed at baseline and longitudinally using a standardized frailty tool, which should guide the intensity and type of nutritional and physical therapy in individual liver transplant candidates. The working group agreed that frailty should not be used as the sole criterion for delisting a patient for liver transplantation, but rather should be considered one of many criteria when evaluating transplant candidacy and suitability. A road map to advance frailty in the clinical and research settings of liver transplantation is presented here.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.016 | 0.014 |
| 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".