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Record W4381333059 · doi:10.1016/j.jhep.2023.05.045

Ductular reaction-associated neutrophils promote biliary epithelium proliferation in chronic liver disease

2023· article· en· W4381333059 on OpenAlexaff
Sílvia Ariño, Beatriz Aguilar‐Bravo, Mar Coll, Woo‐Yong Lee, Moritz Peiseler, Paula Cantallops-Vilà, Laura Sererols-Viñas, Raquel A. Martínez-García de la Torre, Celia Martínez–Sánchez, Jordi Pedragosa, Laura Zanatto, Jordi Gratacós‐Ginès, Elisa Pose, Delia Blaya, Xènia Almodóvar, María Fernández-Fernández, Paloma Ruiz-Blázquez, Juan José Lozano, Silvia Affò, Anna M. Planas, Pere Ginès, Anna Moles, Paul Kubes, Pau Sancho‐Bru

Bibliographic record

VenueJournal of Hepatology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversity of Calgary
FundersH2020 Marie Skłodowska-Curie ActionsNational Institute on Alcohol Abuse and AlcoholismInstituto de Salud Carlos IIIHorizon 2020 Framework ProgrammeNational Institutes of HealthAgència de Gestió d'Ajuts Universitaris i de RecercaFundación Bancaria Caixa d'Estalvis i Pensions de BarcelonaMinisterio de Educación, Cultura y DeporteCentro de Investigación Biomédica en Red de Enfermedades Hepáticas y DigestivasMinisterio de Ciencia e InnovaciónEuropean Regional Development FundEuropean CommissionInstitució Catalana de Recerca i Estudis AvançatsMinisterio de Educación y Formación ProfesionalMinisterio de Ciencia, Innovación y UniversidadesAgencia Estatal de Investigación“la Caixa” Foundation
KeywordsEpitheliumBiliary diseaseChronic liver diseaseMedicinePathologyGastroenterology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.282
Teacher spread0.260 · 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 designBench or experimental
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

Citations58
Published2023
Admission routes1
Has abstractno

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