Patient Views on Advance Care Planning in Cirrhosis: A Qualitative Analysis
Bibliographic record
Abstract
Aim: To investigate patient experiences and perceptions of advance care planning (ACP) process in cirrhosis. Methods: Purposive sampling was used to identify and recruit participants (N = 17) from discrete patient groups: compensated with no prior decompensation, decompensated and not yet listed for transplant, transplant wait listed, medical contraindications for transplant, and low socioeconomic status. Review and discussion of local ACP videos, documents, and experiences with ACP occurred in two individual interviews and four focus groups. Data were analyzed using inductive content analysis including iterative processes of open coding, categorization, and abstraction. Results: Three overarching categories emerged: (1) lack of understanding about disease trajectories and ACP processes, (2) roles of alternate decision makers, and (3) preferences for receiving ACP information. Most patients desired advanced care-planning conversations before the onset of decompensation (specifically hepatic encephalopathy) with a care provider with whom they had a trusting, preexisting relationship. Involvement of the alternate decision makers was of critical importance to participants, as was the use of direct, easy to understand patient education tools that address practical issues. Conclusion: Our findings support the need for early advance care planning in the outpatient setting. Outpatient clinicians may play a key role in facilitating these 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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".