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Record W4361274844 · doi:10.33590/emjhepatol/10305703

Management of Clinically Significant Itch in Cholestatic Liver Disease

2023· article· en· W4361274844 on OpenAlexaff
Cynthia Levy, Gideon M. Hirschfield, Andreas E. Kremer, Kidist Yimam

Bibliographic record

VenueEMJ Hepatology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsToronto Liver CentreUniversity of Toronto
Fundersnot available
KeywordsMedicinePrimary sclerosing cholangitisCholestasisProgressive familial intrahepatic cholestasisLiver transplantationQuality of life (healthcare)Internal medicineGastroenterologyDiseaseLiver diseaseTransplantation

Abstract

fetched live from OpenAlex

Cholestatic liver diseases include primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and progressive familial intrahepatic cholestasis (PFIC). In all of these conditions, cholestatic itch is a major symptom that can severely and chronically impact a person’s quality of life (QoL). At a satellite symposium presented at the 2022 meeting of the American Association for the Study of Liver Diseases (AASLD) in Washington, D.C., USA, leading experts discussed the importance of assessing itch in all patients with one of these cholestatic liver diseases. The experts presented patient cases to illustrate the challenges of managing itch in these cholestatic liver diseases. Studies show that many of these patients are not being adequately treated for this important symptom. However, while there are several treatments for itch, although not all are specifically approved medications, finding the right one for each patient may be a process of trial and error. In some cases, for people with severe, chronic, non-treatment-responsive cholestatic itch, a liver transplant may be the only treatment option.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.041
GPT teacher head0.336
Teacher spread0.294 · 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
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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