Validation of Montreal Cognitive Assessment-Basic in a sample of elderly Egyptians with neurocognitive disorders
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Montreal Cognitive Assessment-Basic (MoCA-B) is a modified version of the MoCA that is especially suitable for use in elderly subjects with low education. The Authors translated the tool into Arabic and they aimed at validation of this tool in a sample of elderly Egyptians. METHODS: The study included 93 patients, 60 years and older, fulfilling the DSM-5 criteria of Mild Neurocognitive Disorder (NCD) (39 patients) and Major Neurocognitive Disorder (54 patients) that were compared to 112 community dwelling elder subjects. All subjects were assessed using the MoCA-B, Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale (CDR) in addition to the required laboratory and radiological investigations. RESULTS: MoCA-B demonstrated good internal consistency (Cronbach's alpha = 0.915) and content validity in discrimination between normal and diseased subjects. It showed superior sensitivity and specificity when compared to MMSE in screening for Mild NCD (AUC MoCA-B = 0.988 versus MMSE = 0.939). The recommended cut-off was 21/22 with sensitivity of 92.5% and specificity of 98.2% for detecting Mild NCD and 16/17 with sensitivity of 90.7% and specificity of 97.4% for detecting Major NCD (dementia). CONCLUSION: The Arabic MoCA-B is a valid cognitive assessment tool in elderly Egyptian subjects.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".