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Record W4308303110 · doi:10.14309/ajg.0000000000001999

Berberine Ursodeoxycholate for the Treatment of Primary Sclerosing Cholangitis: The Search for the Elusive Pharmacologic Holy Grail Will Need to Continue

2022· article· en· W4308303110 on OpenAlexaff
Eric M. Yoshida

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUrsodeoxycholic acidMedicinePrimary sclerosing cholangitisHoly GrailAlkaline phosphatasePrimary biliary cirrhosisInternal medicineGastroenterologyPlaceboPathologyDiseaseAlternative medicineBiochemistryEnzyme

Abstract

fetched live from OpenAlex

ABSTRACT: Effective pharmacologic treatment of primary sclerosing cholangitis (PSC) remains elusive. Ursodeoxycholic acid (UDCA) is known to improve liver biochemistry, specifically serum alkaline phosphatase, in patients with PSC but has not been shown to favourably alter the natural history. Similarly, many immunomodulatory medications have been studied for the treatment of PSC, but none has been demonstrated to be of unequivocal benefit. In this issue of the Journal, a pilot study of a ursodeoxycholate berberine salt vs placebo is reported. Although improvement in serum alkaline phosphatase is reported, without a control arm with UDCA monotherapy, it is not possible to determine whether this study drug is beneficial over UDCA by itself. More study in the PSC therapeutic arena is needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.030
GPT teacher head0.307
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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