Integrating Specialist Palliative Care in the Liver Transplantation Evaluation Process: A Qualitative Analysis of Hepatologist and Palliative Care Provider Views
Bibliographic record
Abstract
Patients undergoing evaluation for liver transplantation face heavy burdens of symptoms, health care use, and mortality. In other similarly ill populations, specialist palliative care has been shown to benefit patients, but specialist palliative care is infrequently used for liver transplantation patients. This project aims to describe the potential benefits of and barriers to specialist palliative care integration in the liver transplantation process. We performed qualitative analysis of transcripts from provider focus groups followed by a community engagement studio of patients and caregivers. Focus groups consisted of 14 palliative care specialists and 10 hepatologists from 11 institutions across the United States and Canada. The community engagement studio comprised patients and caregivers of patients either currently on the liver transplantation waiting list or recently after transplant. The focus groups identified 19 elements of specialist palliative care that could benefit this patient population, including exploring patients' illness understanding and expectations; assessing physical symptoms comprehensively; discussing patient values; and providing caregiver support, a safe space to discuss noncurative options, and anticipatory guidance about likely next steps. Identified barriers included role boundaries, differences in clinical cultures, limitations of time and staff, competing goals and priorities, misconceptions about palliative care, limited resources, changes in transplant status, and patient complexity. Community studio participants identified many of the same opportunities and barriers. This study found that hepatologists, palliative care specialists, patients, and caregivers identified areas of care for liver transplantation patients that specialist palliative care can improve and address.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".