Review of pharmacotherapeutic treatments for primary sclerosing cholangitis
Bibliographic record
Abstract
Background: The objective of this review was to evaluate pharmacotherapeutic treatments for primary sclerosing cholangitis (PSC) through a literature search of current published data. A review of the current clinical data for each treatment is discussed. METHODS: We conducted a systematic literature search for articles using EMBASE (1980 to April 1, 2018), and MEDLINE (1948 to April 1, 2018) using Ovid, to identify studies investigating various therapies in PSC. Search terms included the following: primary sclerosing cholangitis, cholangitis, sclerosing cholangitis; ursodeoxycholic acid, glucocorticoids, cyclosporine, tacrolimus, methotrexate, azathioprine, 6-mercaptopurine, penicillamine, anti-TNF, antibiotics, and probiotics. We also performed a review of current clinical trials using ClinicalTrials.gov. We considered for review relevant studies published in English, pilot studies, and randomized controlled trials involving human subjects. RESULTS: Therapies that have been investigated in the management of PSC include those used in search terms and others that were not included in our search parameters. Analysis of published data involving each therapy was explored and none have shown any sustained, significant benefit in the treatment of PSC. In terms of relevance to patient care and clinical practice, this review evaluates and compares various pharmacotherapeutic treatments for PSC where liver transplantation remains the only definitive treatment. CONCLUSIONS: To date, no clinical study of any drug has demonstrated effectiveness in terms of survival benefit or a decreased need for liver transplantation. More clinical studies are needed, and patients need to be adequately informed before any medical therapy for PSC is undertaken.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".