Cognitive impairment in hemodialysis patients: Implementation of cut-off values for the Montreal Cognitive Assessment (MoCA)-test for feasible screening
Bibliographic record
Abstract
OBJECTIVE: Reliable identification of cognitive impairment in hemodialysis patients is of utmost importance, as it is associated with poor outcomes including dialysis withdrawal and death. High prevalence of cognitive impairment has been demonstrated in several studies using brief screening instruments or neuropsychological test batteries. However, the relevance of cognitive impairment as well as the accuracy of screening procedures have never been studied in this patient population. METHODS: 151 chronic hemodialysis patients (mean age 65.78 ± 14.88 years, 73,5% male) underwent cognitive testing under standardized conditions by the Montreal Cognitive Assessment (MoCA) and, in a second step, the Clinical Dementia Rating scale (CDR), an international standard to measure the severity of dementia. For calculating MoCA cut-off values on the basis of the CDR global score, receiver operator characteristics (ROC) analysis and c-statistic were applied. RESULTS: 49.0% of patients were categorized as 0.5 in the CDR global with memory being the predominantly affected domain (47.7% of patients scored ≥ 0.5). Youden's Index led to a threshold of 23.5 points for the MoCA test for optimal differentiation between cognitively normal (CDR global < 0.5) and impaired patients (CDR global ≥ 0.5) based on a sensitivity of approximately 99% and a specificity of approximately 74%. CONCLUSION: Interference of cognitive impairment with patients' independence and daily life was shown using the CDR for the first time in hemodialysis patients. A MoCA score of 23.5 points turned out as optimal threshold to differentiate between patients with and without functional impairment in the CDR, thereby paving the way for implementation of the MoCA test as a quick and thus highly feasible screening instrument for periodic testing in clinical routine.
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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 | 0.006 | 0.013 |
| 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 | 0.001 | 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".