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Record W2912908925 · doi:10.1016/j.cgh.2019.01.026

Alcohol-Related Liver Disease Is Rarely Detected at Early Stages Compared With Liver Diseases of Other Etiologies Worldwide

2019· article· en· W2912908925 on OpenAlexaff
Neil Shah, Meritxell Ventura‐Cots, Juan G. Abraldeṣ, Mohamed Alboraie, Ahmad Alfadhli, Josepmaria Argemí, E. Aranda, Enrique Arús-Soler, A. Sidney Barritt, Fernando Bessone, Marina Biryukova, Flair José Carrilho, Zaily Dorta Guridi, Mohamed El‐Kassas, Teo Eng-Kiong, Alberto Queiróz Farias, Jacob George, Wenfang Gui, Prem Harichander Thurairajah, John Hsiang, Azra Husić-Selimović, Isakov Va, Mercy Karoney, Won Kim, Johannes Kluwe, Rakesh Kochhar, Narendra Dhaka, Pedro Marques da Costa, Mariana A. Nabeshima Pharm, Ono S, Daniela Reis, Agustina Rodil, Caridad Ruenes Domech, F. Sáez-Royuela, Christoph Scheurich, Way Siow, Nadja Sivac-Burina, Edna Solange Dos Santos Traquino, Fatma Some, Sanjin Sprečkić, Shiyun Tan, Julio Vorobioff, Andrew Wandera, Pengbo Wu, Mohamed Yacoub, Ling Yang, Yuanjie Yu, N. Zahiragic, Chaoqun Zhang, Helena Cortez‐Pinto, Ramón Bataller

Bibliographic record

VenueClinical Gastroenterology and Hepatology · 2019
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of Alberta
FundersNational Institute on Alcohol Abuse and AlcoholismNational Natural Science Foundation of ChinaAsociación Española para el Estudio del Hígado
KeywordsMedicineEtiologyLiver diseaseAlcoholic liver diseaseDiseaseInternal medicineGastroenterologyPathologyCirrhosis

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.075
GPT teacher head0.361
Teacher spread0.287 · 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 designObservational
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

Citations154
Published2019
Admission routes1
Has abstractno

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