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Record W4380301315 · doi:10.1016/j.jhep.2023.05.028

The novel SALT-M score predicts 1-year post-transplant mortality in patients with severe acute-on-chronic liver failure

2023· article· en· W4380301315 on OpenAlexaff
Rubén Hernáez, Constantine Karvellas, Yan Liu, Sophie‐Caroline Sacleux, Saro Khemichian, Lance L. Stein, Kirti Shetty, Christina C. Lindenmeyer, Justin Boike, Douglas A. Simonetto, Robert S. Rahimi, Prasun K. Jalal, Manhal Izzy, Michael Kriss, Gene Y. Im, Ming Lin, Janice H. Jou, Brett E. Fortune, George Cholankeril, Alexander Kuo, Nadim Mahmud, Fasiha Kanwal, Faouzi Saliba, Vinay Sundaram, Thierry Artzner, Rajiv Jalan, Atef Al Attar, Kambiz Kosari, Richard Garcia, Gevork Salmastyan, William Cranford, Preet Patel, Pei Xue, Soumya Mishra, Madison Parks, Gianina Flocco, Jing Gao, Tiffany Wu, Priya Thanneeru, Mariana Hurtado, Islam Mohamed, Ross Vyhmeister, Christine R. Lopez, Braidie Campbell, Adam C. Winters, Mary Ann Simpson, Xiaohan Ying

Bibliographic record

VenueJournal of Hepatology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCenter for Geosphere Dynamics, Charles UniversityCenter for Innovations in Quality, Effectiveness and SafetyNational Cancer InstituteNational Institutes of HealthGrifolsUniverzita Karlova v PrazeHealth Services Research and DevelopmentVeterans Administration Medical CenterCancer Prevention and Research Institute of Texas
KeywordsMedicineCohortLiver transplantationInternal medicineDiabetes mellitusTransplantation

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.243
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

Citations51
Published2023
Admission routes1
Has abstractno

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