P184 The need for a more holistic approach to managing patients with autoimmune hepatitis
Bibliographic record
Abstract
Introduction Patients with chronic disease often have complex medical and psychosocial needs. Data shows that patients with autoimmune hepatitis (AIH) have impaired quality of life. This International AIH Survey on Patients’ Views and Experiences collected information about the support mechanisms that are currently available. Methods Clinicians and patient representatives designed the survey. An electronic weblink was disseminated by AIH Support, LiverNorth and the British Liver Trust, for anonymous data collection from patients in any country. Thematic qualitative and descriptive data analyses were undertaken. Results A total of 270 responses were received (median age 55 [range 17–83 years], 94% female). Almost half (49%) reported being embarrassed to tell people that they have AIH. The majority attributed this to the stigma surrounding liver disease and the perception that their disease is self-inflicted or infectious (many suggested a name change from hepatitis). 53% worry about their disease either all or a lot of the time and 56% worry about the effect their AIH medication has on them either all or a lot of the time. A third of patients reported that worrying about the future is the most difficult aspect of living with AIH. Fatigue was the most frequent answer when asked about frustrations and difficulties associated with AIH. In terms of patient support, 47% had accessed patient groups, with 79% being AIH-specific. This may be influenced by the route of survey dissemination and higher than in the whole patient community. The average helpfulness score was 7.9 (1 not helpful - 10 extremely helpful). Only 19% had access to a specialist liver nurse. Key themes were better access to specialist care, improved communication, proper acknowledgement of symptoms and more research to find better treatments with fewer side effects and, ultimately, a cure. Conclusions Medical care often focusses on disease control but this data highlights important factors that impact on patients’ experiences of AIH. The stigma associated with liver disease and not feeling adequately informed about their treatments or prognosis leads to significant anxiety. A more holistic approach to care is needed and signposting towards support groups can be very valuable for patients.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".