What are the lived healthcare experiences of patients with primary sclerosing cholangitis? A community-based qualitative interview study
Bibliographic record
Abstract
OBJECTIVES: Primary sclerosing cholangitis (PSC) is a rare chronic disease that presents challenges to both patients and clinicians. This study aimed to explore the experiences of patients with PSC regarding their disease and healthcare. DESIGN: A series of semi-structured interviews was completed with patients with PSC, including questioning their experiences of living with PSC and its related healthcare. SETTING: Participants were recruited from communities in England, Scotland and Wales, with advertisement via PSC Support (UK disease-specific charity support group). PARTICIPANTS: 18 patients aged between 21 and 72 years were interviewed; 10 were male (56%), and all were of Caucasian ethnicity. Inclusion criteria were as follows: adults, self-identifying as having a diagnosis of PSC, and currently under National Health Service treatment for this disease. Patients with a history of liver transplantation were excluded. RESULTS: Participants confirmed the ongoing physical and psychological burden of PSC and its related healthcare. Living with PSC was described as a journey; the timeline of events was important to patients, with particular challenges identified along the way. These included difficulties in obtaining a diagnosis and accessing timely and knowledgeable medical care. Overcoming the unusual combination of uncertainties that PSC presents was of particular concern to participants; these differ from those observed in more common chronic diseases with established treatment pathways. Hidden complexities within chronic illness behaviour in PSC were described, including a noteworthy fragile doctor-patient relationship and dependence on the specialist. These produce additional challenges for the optimal clinical management of such patients by generalists and specialists. CONCLUSIONS: This study complements the existing literature on the ongoing high burden of PSC, with added value from in-depth discussions with patients themselves. Priorities for further work have been identified, including the need for improved risk stratification tools to allow individualised management and prognostication, as well as improving access to knowledgeable care while maintaining a strong doctor-patient relationship.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".