Development of a Question Prompt List for People Living With Primary Biliary Cholangitis: A Delphi Study
Bibliographic record
Abstract
BACKGROUND & AIMS: Clinical practice guidelines support caregivers to manage liver diseases. However, people with lifelong conditions often lack guidance to understand what aspects of care are most important and how their disease should be managed. This study aimed to create a question prompt list (QPL) for individuals with primary biliary cholangitis (PBC) including key questions (directed) to their treating physician that are most likely to improve their outcome. METHODS: International PBC professionals including patient representatives rated and ranked questions related to 9 aspects of PBC care. Questions rated by >70% as moderately/very important were considered best candidate questions (BCQs) for the QPL. Results of the survey were discussed during 2 in-person meetings, upon which the questions and/or QPL were amended. RESULTS: Based on 108 respondents, 11 of 43 questions were considered BCQs. After 2 in-person meetings (64 attendees), the final QPL contained 8 questions and was unanimously approved by 19 members of the study team during the consensus meeting. The included questions referred to the risk of disease progression, presence of cirrhosis, need of second-line therapy, need of repeated liver stiffness measurements, bone health, and availability of patient information and support. Two BCQs addressing options to manage pruritus and fatigue were combined on the QPL. In addition, one question regarding first-line therapy was included despite being rated as moderately/very important by 68.5%. CONCLUSIONS: The PBC patient question prompt list serves as a user-facing document, to enhance the patient experience, and drive value-based healthcare in routine clinical practice.
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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 | 0.171 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".