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Record W4389981755 · doi:10.1111/ctr.15229

Prevalence of cognitive impairment in liver transplant recipients

2023· article· en· W4389981755 on OpenAlexaboutno aff
Kacey Berry, Jessica M. Ruck, Fawzy Barry, Amy M. Shui, Aly Cortella, Dorothea Kent, Srilakshmi Seetharaman, Randi Wong, Lawren VandeVrede, Jennifer C. Lai

Bibliographic record

VenueClinical Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on Aging
KeywordsCognitive impairmentMedicineLiver transplantationCognitionInternal medicinePsychiatryTransplantation

Abstract

fetched live from OpenAlex

Liver transplant (LT) recipients have a high burden of cognitive impairment risk factors identified in other populations, yet little work has explored cognition in the United States LT population. We characterized prevalence of cognitive impairment (CI) in LT recipients pre-LT and ≥3 months post-LT. Adult LT recipients with cirrhosis but without active pre-LT hepatic encephalopathy (HE) were screened for CI using the Montreal Cognitive Assessment (MoCA) for CI (MoCA <24) both pre-LT and ≥3 months post-LT. The association between cognitive performance and recipient characteristics was assessed using logistic regression. Of 107 LT recipients, 36% had pre-LT CI and 27% had post-LT CI [median (Q1-Q3) MoCA 26 (23-28)]. Each 1-point increase in pre-LT MoCA was associated with 26% lower odds of post-LT cognitive impairment (aOR .74, 95% CI .63-.87, p < .001), after adjusting for recipient age, history of HE, and time since LT. In this study of cirrhosis recipients without active pre-LT HE, cognitive impairment was prevalent before LT and remained prevalent ≥3 months after LT (27%), long after effects of portal hypertension on cognition would be expected to have resolved. Our data expose an urgent need for more comprehensive neurologic examination of LT recipients to better identify, characterize, and address predictors of post-LT cognitive impairment.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.061
GPT teacher head0.380
Teacher spread0.319 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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