Quality measures in pre-liver transplant care by the Practice Metrics Committee of the American Association for the Study of Liver Diseases
Bibliographic record
Abstract
The liver transplantation (LT) evaluation and waitlisting process is subject to variations in care that can impede quality. The American Association for the Study of Liver Diseases (AASLD) Practice Metrics Committee (PMC) developed quality measures and patient-reported experience measures along the continuum of pre-LT care to reduce care variation and guide patient-centered care. Following a systematic literature review, candidate pre-LT measures were grouped into 4 phases of care: referral, evaluation and waitlisting, waitlist management, and organ acceptance. A modified Delphi panel with content expertise in hepatology, transplant surgery, psychiatry, transplant infectious disease, palliative care, and social work selected the final set. Candidate patient-reported experience measures spanned domains of cognitive health, emotional health, social well-being, and understanding the LT process. Of the 71 candidate measures, 41 were selected: 9 for referral; 20 for evaluation and waitlisting; 7 for waitlist management; and 5 for organ acceptance. A total of 14 were related to structure, 17 were process measures, and 10 were outcome measures that focused on elements not typically measured in routine care. Among the patient-reported experience measures, candidates of LT rated items from understanding the LT process domain as the most important. The proposed pre-LT measures provide a framework for quality improvement and care standardization among candidates of LT. Select measures apply to various stakeholders such as referring practitioners in the community and LT centers. Clinically meaningful measures that are distinct from those used for regulatory transplant reporting may facilitate local quality improvement initiatives to improve access and quality of care.
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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 | 0.246 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".