Primary Sclerosing Cholangitis With Features of Autoimmune Hepatitis: Exploring the Global Variation in Management
Bibliographic record
Abstract
Patients with primary sclerosing cholangitis (PSC) frequently manifest features of autoimmune hepatitis (AIH). We sought to understand factors affecting expert management, with the goal of facilitating uniformity of care. A Survey Monkey questionnaire with four hypothetical cases suggesting a potential AIH/PSC variant was sent to hepatologists spanning global practices. Eighty responses from clinicians in 23 countries were obtained. Most of the respondents would request a liver biopsy, and stated that the cases presented could not be appropriately managed without a biopsy. Despite the fact that histology did not unequivocally support an AIH/PSC variant in three of the four cases, this diagnosis was reached by most of the respondents for all cases, except case 1, in which 49% were diagnosed with AIH/PSC. There was a wide variation of suggested medical treatment. For three cases, the most commonly chosen treatment options did not exceed 35%, indicating a lack management consensus. Most respondents would treat with ursodeoxycholic acid, despite current American Association for the Study of Liver Diseases guidelines, either alone or in combination with immunosuppression. European clinicians recommended ursodeoxycholic acid more frequently than their counterparts in North America (P < 0.05 in three out of four cases), who advocated the use of immunosuppression alone more commonly than Europeans (P = 0.005 in case 2). Conclusions: We document a wide variation in clinical decision making in the context of managing patients with a potential AIH/PSC variant. Guidance, likely based on systematic studies arising from prospective registries, is needed to better address this difficult clinician problem.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".