Psychometric properties of the Montreal Cognitive Assessment (MoCA) in inpatient liver transplant candidates
Bibliographic record
Abstract
Hepatic encephalopathy (HE) is a consequence of liver disease and often diagnosed via psychometric testing. With inpatients, the Montreal Cognitive Assessment (MoCA) may be used as part of cognitive screening for transplant candidacy. However, the MoCA was developed to detect mild cognitive impairment in aging populations and its psychometric properties in inpatients with liver disease have not been determined. Retrospective chart review identified inpatient liver transplant candidates who were administered a MoCA as part of their neuropsychological screening and had either no cognitive dysfunction or a diagnosis of HE made by a neuropsychologist (n = 57, mean age = 48.8 ± 12.6 years). Psychometric analyses were conducted and regression analysis was performed to determine the predictive value of different variables on total MoCA scores. Internal consistency of MoCA domain scores was good (α = 0.80). Significant inverse relationships were found with Trail Making Test, Parts A and B (r’s = −0.43 and −0.71, respectively). A cutoff score of 24 or below had the best sensitivity (0.72) and specificity (0.77) for identifying those with a diagnosis of HE. Increasing age and the presence of altered mental status were the strongest predictors of lower MoCA scores (both p’s < 0.05, ηp2 = 0.10–0.14). The MoCA is appropriate to use with inpatient liver transplant candidates, with a cutoff of 24 or below to detect abnormal cognition. In addition to the clinical interview and other neuropsychological tests (including, but not limited to, the Trail Making Test, Parts A and B), low MoCA scores can help determine the presence of HE.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".