The Liver Frailty Index: a model for establishing organ-specific frailty metrics across all solid organ transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: In this review, we discuss the development of the Liver Frailty Index (LFI) and how it may serve as a model for developing other organ-specific frailty indices. RECENT FINDINGS: As the demand for solid organ transplants continues to increase, the transplantation community is enhancing its strategies for organ allocation to gain deeper insights into patient risk profiles and anticipated outcomes. Frailty has emerged as a critical concept in transplant care, offering valuable insights into adverse health outcomes. Standardizing frailty assessment across transplant programs could enhance prognostic accuracy and inform pretransplant interventions.The LFI comprises of three performance-based tests that each represents essential components of the multidimensional frailty construct. This composite metric provides insights beyond liver function and considers nonhepatic comorbid factors. Identifying common frailty principles among all transplant candidates and adopting the LFI methodology, which assesses fundamental frailty principles using liver-specific tools, could establish a foundational pool of shared core frailty principles. From this pool, organ-specific frailty indices could be derived, each equipped with the clinically relevant organ-specific tools to evaluate common core principles. SUMMARY: Creating a standardized framework across all solid-organ transplants, with common principles and organ-specific measurements, would facilitate consistent frailty assessment, standardize the integration of the frailty construct into transplant decision-making, and enable center-level interventions to improve outcomes for patients with end-stage organ disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".