Standardization and Validation of Montreal Cognitive Assessment (MoCA) in the Moroccan Population
Bibliographic record
Abstract
Background: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test designed to assist health professionals in the detection of mild cognitive impairment and Alzheimer's disease (AD). Objectives: The aim of our work was to perform the adaptation and standardization of the MoCA in the Moroccan population, taking into account its different demographic characteristics, i.e., age, gender and education level. The second aim was to evaluate the predictive validity of the test in Moroccan patients with Alzheimer’s disease. Patients and Methods / Material and Methods: First we administered the MoCA-ma to 120 normal participants (60 men and 60 women). All the participants can read and speak Arabic, they had no neurological, neuropsychological, psychiatric or toxic history and they had a preserved cognitive functioning. Subjects were categorized according to age and educational level. Secondly, we administered the MoCA-ma and the Mini-Mental State Examination (MMSE-ma) to 40 healthy controls and 40 subjects fulfilling diagnostic criteria for AD. All the patients diagnosed as having AD underwent complete neurologic and somatic clinical examination, usual laboratory testing and MRI. Results: The MoCA-ma norms were established considering significant influential factors. Indeed, the normative data in this version have shown that performance of normal participants depend mainly on age and level of education while gender had no significant influence. The results of validation showed that the MoCA-ma was sensitive enough to detect cognitive impairment in subjects with AD. Conclusion: The standardization and validation of the Arabic version of the MoCA-ma provides to physicians an useful brief cognitive screening tool for the detection of AD in the Arabic countries.
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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 | 0.008 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".