Performance of dialysis patients on the standard and basic Arabic versions of the Montreal Cognitive Assessments
Bibliographic record
Abstract
Objectives: To assess cognitive performance in Saudi patients on dialysis using Arabic versions of the Montreal Cognitive Assessment (MoCA) and assess the reliability of the scales. Methods: We performed a cross-sectional study at the dialysis unit of King Saud University Medical City, Riyadh from April 2019 to March 2020. Patients ≥ 18 years of age with no history of dementia underwent cognitive assessment with the standard (MoCA-A) and basic (MoCA-B) Arabic versions, with repeat testing in a subset of participants. Results: Recruitment included 83 participants, 56 on hemodialysis (HD) and 27 on peritoneal dialysis (PD). The mean±SD for age was 49.99 (15.48), and for years of education was 10.29 (5.5). The mean score for MoCA-A was 21.03±5.35, and for MoCA-B was 23.45±5.14. Younger age, longer years of education and peritoneal dialysis were significantly associated with higher MoCA scores on both versions (p<0.05). The ICC was 0.81 (95% CI 0.65, 0.91) and 0.77 (95% CI 0.58, 0.89) for MoCA-A and MoCA-B, respectively. The performance on the executive and calculation tasks were higher in the PD group on the MoCA-B. The recall mean score was higher in the PD group on the MoCA-A. Conclusion: The HD patients are at higher risk for cognitive impairment compared to PD patients. Age and education are important variables influencing performance. Both Arabic versions of the MoCA are reliable screening tools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".