Developing Priorities for Palliative Care Research in Advanced Liver Disease: A Multidisciplinary Approach
Bibliographic record
Abstract
Individuals with advanced liver disease (AdvLD), such as decompensated cirrhosis (DC) and hepatocellular carcinoma (HCC), have significant palliative needs. However, little research is available to guide health care providers on how to improve key domains related to palliative care (PC). We sought to identify priority areas for future research in PC by performing a comprehensive literature review and conducting iterative expert panel discussions. We conducted a literature review using search terms related to AdvLD and key PC domains. Individual reviews of these domains were performed, followed by iterative discussions by a panel consisting of experts from multiple disciplines, including hepatology, specialty PC, and nursing. Based on these discussions, priority areas for research were identified. We identified critical gaps in the available research related to PC and AdvLD. We developed and shared five key priority questions incorporating domains related to PC. Conclusion: Future research endeavors focused on improving PC in AdvLD should consider addressing the five key priorities areas identified from literature reviews and expert panel discussions.
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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 | 0.135 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".