MétaCan
Menu
Back to cohort
Record W4412605372 · doi:10.1053/j.gastro.2025.07.015

Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation

2025· article· en· W4412605372 on OpenAlexaff
Scott Silvey, Patrick S. Kamath, Jacob George, Ashok Choudhury, Qing Xie, Mark Topazian, Hailemichael Desalgn Mekonnen, Zhujun Cao, Aabha Nagral, K. Rajender Reddy, Danielle Adebayo, Sumeet K. Asrani, Neil Rajoriya, Marco Arrese, Sevda Aghayeva, Mithun Sharma, Sarai Gonzalez Huezo, Adrián Gadano, Hasan Basri Yapıcı, Nabil Debzi, Jawaid Shaw, Somaya Albhaisi, José Luis Pérez Hernández, Yingling Wang, Feng Peng, Linlin Wei, CE Eapen, Hiang Keat Tan, James Fung, Ruveena Bhavani Rajaram, Kessarin Thanapirom, Haydar Adanır, Adam Doyle, S. Shalimar, Minghua Su, Dinesh Jothimani, Yijing Cai, René Malé Velazquez, Wei Wang, Michael Gounder, Cameron Gofton, Sezgin Barutçu, Büşra Haktanıyan, Alberto Queiróz Farias, Aloysious Aravinthan, Chinmay Bera, Surender Singh, Peter Hayes, Ramazan Idılman, Aldo Torre, Mário Reis Álvares‐da‐Silva, Wai‐Kay Seto, Florence Wong, Brian J. Bush, Leroy R. Thacker, Nilang Patel, Jasmohan S. Bajaj

Bibliographic record

VenueGastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsCirrhosisCohortProspective cohort studyMedicineIntensive care medicineEmergency medicineInternal medicine

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)0.009
metaresearch head score (Gemma)0.016
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 score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.236
Teacher spread0.231 · 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

Citations9
Published2025
Admission routes1
Has abstractno

Explore more

Same venueGastroenterologySame topicLiver Disease and TransplantationFrench-language works237,207