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Record W7106529647 · doi:10.5281/zenodo.17702014

Open Drug Discovery to Treat Primary Sclerosing Cholangitis

2025· other· en· W7106529647 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsPrimary sclerosing cholangitisDrug developmentDrug discoveryDrugPopulationDisease

Abstract

fetched live from OpenAlex

Primary Sclerosing Cholangitis (PSC) is a rare liver disease with no approved treatments and a significant unmet medical need. The limited patient population makes investment in drug development for PSC challenging. This project seeks to address this by taking a collaborative open science approach to advance new treatments for this rare disease, which currently has only a limited number of active pharmaceutical programs.The investigators have developed the first transcriptomic map of the PSC liver, providing an unprecedented view of the disease mechanisms at the cellular level. This work has enabled the identification of known targets and their corresponding drugs, highlighting SYK inhibition as an avenue to target immune-mediated fibrotic pathways within the PSC liver. This project will test the ability of SYK inhibitors to modulate fibrogenic processes in human explanted PSC tissue in comparison to healthy human liver.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score1.000
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.005

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 head0.037
GPT teacher head0.323
Teacher spread0.286 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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