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Record W4416808639 · doi:10.1016/j.jceh.2025.103421

Screening for Malnutrition, Sarcopenia, and Physical Frailty Beyond One Year after Liver Transplantation

2025· article· en· W4416808639 on OpenAlexaff
Amal Trigui, Crystèle Hogue, Mélanie Tremblay, Geneviève Huard, Christopher F. Rose, Chantal Bémeur

Bibliographic record

VenueJournal of Clinical and Experimental Hepatology · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsLiver transplantationQuality of life (healthcare)Psychological interventionMEDLINETransplantation

Abstract

fetched live from OpenAlex

Background/Aims: Malnutrition, sarcopenia, and frailty negatively impact quality of life and increase mortality following liver transplantation (LT). However, long-term follow-up data remain limited. This study aimed primarily to assess the malnutrition risk at 1-, 2-, and 3-years post-LT. Secondary objectives included evaluating the sarcopenia and frailty risk, muscle function, physical activity, quality of life, and employment status at the same time points. Methods: This cross-sectional study included LT recipients with a history of cirrhosis, transplanted between January 2019 and December 2021. Each participant completed a single virtual meeting during which nutritional risk, sarcopenia, frailty risk, muscle function, physical activity, quality of life, employment status, and dietary intakes were assessed. Results: < 0.001, respectively). Only 28.0%, 42.9%, and 25.9% of participants in cohorts A, B, and C, respectively, returned to work. Conclusion: Up to 3 years after LT, patients were still at risk of malnutrition, sarcopenia, and frailty. The results of this study highlight the need for targeted interventions to improve outcomes and support long-term quality of life post-LT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.089
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.452
Teacher spread0.357 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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