Normative and Equated Data of the Original and Basic Versions of the Montreal Cognitive Assessment among Community Dwelling Saudi Arabians
Bibliographic record
Abstract
Introduction. Currently, there are standard and basic versions of the MoCA, the latter designed for those with lower educational achievements. Community-based normative data on these versions of the MoCA from Arabic populations are deficient, and there is little data demonstrating how both scales perform in comparison. We aim to obtain normative performances from both versions and equate the measures of both scales. Methods. Community-based recruitment of healthy volunteers ≥ 18 years of age. Participants underwent testing with both versions. Demographic data was collected with regard to age, gender, years of education, diabetes, and hypertension. Regression analysis was performed to determine significance of variables, and the circle-arc equating method was used to equate the two scores from each scale. Results. 311 participants were included in the study. The mean (sd) age was 45.8 (15.96), females were 184 (59.16%), and the duration of education was 12.7 (5.67) years. The mean scores on the MoCA-A and MoCA-B were 21.47 (4.53) and 24.37 (4.71) ( P < 0.0001 ), respectively. Multivariate regression showed significance of age and years of education in both versions (both variables with P < 0.0001 ). Correlation coefficient between the two scales was 0.77 ( P < 0.0001 ). The largest equated difference between both MoCA versions was four points in those scoring from 10-20 on the MoCA-A. Conclusion. We present normative data from a large Saudi Arabian community-based sample with two different MoCA tests, and an equating graph is presented to determine the corresponding expected performance between the two scales.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".