Validity and Reliability of MoCA-Ina for Assessing Cognitive Function in Dialysis Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Montreal Cognitive Assessment (MoCA) is widely used for assessing cognitive function in chronic kidney disease (CKD) patients, but its effectiveness in Indonesian CKD populations remains unclear compared to studies in other regions. OBJECTIVES: To evaluate the reliability and validity of the translated MoCA in assessing cognitive impairment among dialysis patients. METHODS: This cross-sectional study was conducted at the hemodialysis and Continuous Ambulatory Peritoneal Dialysis (CAPD) unit of RSUD Dr. Saiful Anwar in Indonesia from January to February 2024. The study utilized the Indonesian Version of MoCA for cognitive assessment. Reliability and validity of the questionnaire were evaluated using Cronbach's Alpha and Pearson validity test methods. RESULTS: In this study, 43 participants were enrolled, including 21 undergoing hemodialysis and 22 receiving CAPD. MoCA's reliability was confirmed with Cronbach's Alpha values of 0.648 for hemodialysis and 0.737 for CAPD, indicating strong internal consistency. The questionnaire exhibited favorable discriminatory power, with corrected item-total correlation scores exceeding 0.3 for all items in both groups. Validity demonstrated strong construct validity, with critical values surpassing standard references. All statistical significance levels were below 0.05, affirming MoCA's reliability in assessing cognitive function in dialysis patients. CONCLUSION: In conclusion, our study has demonstrated that the translated MoCA is valid and reliable for assessing cognitive function in CKD patients undergoing dialysis.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".