Predicting Hepatic Encephalopathy-Related Hospitalizations Using a Composite Assessment of Cognitive Impairment and Frailty in 355 Patients With Cirrhosis
Bibliographic record
Abstract
INTRODUCTION: Hepatic encephalopathy (HE) is the most common potentially modifiable reason for admission in patients with cirrhosis. Cognitive and physical components of frailty have pathophysiologic rationale as risk factors for HE. We aimed to assess the utility of a composite score (MoCA-CFS) developed using the Montreal Cognitive Assessment (MoCA) and the Clinical Frailty Scale (CFS) for predicting HE admissions within 6 months. METHODS: Consecutive adult patients with cirrhosis were followed for 6 months or until death/transplant. Patients with overt HE and dementia were excluded. Primary outcome was the prediction of HE-related admissions at 6 months. RESULTS: A total of 355 patients were included; mean age 55.9 ± 9.6; 62.5% male; Hepatitis C and alcohol etiology in 64%. Thirty-six percent of patients had cognitive impairment according to the MoCA (≤24) and 14% were frail on the CFS (>4). The MoCA-CFS independently predicted HE hospitalization within 6 months, a MoCA-CFS score of 1 and 2 respectively increasing the odds of hospitalization by 3.3 (95% CI:1.5-7.7) and 5.7 (95% CI:1.9-17.3). HRQoL decreased with increasing MoCA-CFS. Depression and older age were independent predictors of a low MoCA. CONCLUSIONS: Cognitive and physical frailty are common in patients with cirrhosis. In addition to being an independent predictor of HE admissions within 6 months, the MoCA-CFS composite score predicts impaired HRQoL and all-cause admissions within 6 months. These data support the predictive value of a "multidimensional" frailty tool for the prediction of adverse clinical outcomes and highlight the potential for a multi-faceted approach to therapy targeting cognitive impairment, physical frailty and depression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".