Performance of a Sample of Low Educated and Illiterate Egyptian Elderly in Montreal Cognitive Assessment-Basic.
Bibliographic record
Abstract
Background: Most older adults with dementia reside in low- and middle-income countries where illiteracy and low educational background are common. It is quite challenging to diagnose dementia and mild cognitive impairment in this specific group for multiple reasons including difficult access to health care resources and the availability of an appropriate cognitive tests without high verbal and educational demands.Aim: to evaluate the performance of low-educated and illiterate elderly in Egypt using the Montreal Cognitive Assessment Basic (MoCA-B).Patients and methods: cross-sectional design included 100 elderly participants recruited from Ain Shams University clinics especially geriatrics clinic. Demographic data of the participants were collected. Assessment of cognition with grading the severity of cognitive impairment by clinical dementia rating (CDR) was done, with assessment of cognition by Montreal Cognitive Assessment Basic (MoCA-B) to evaluate performance and diagnostic accuracy.Results: Diagnostic performance of MOCA-B score was moderate in all the participants, high in 6-9 years and 1-5 years of education, and lowest in illiterates. The cut point in participants with 6-9 years of education was lower than that of standard MOCA-B scoring, to be ≤23, it decreased to be ≤22 in participants with 1-5 years of education, then to ≤21 in illiterate participants.Conclusions: The Arabic MoCA-B has proven a moderate diagnostic performance for detecting Mild Cognitive Impairment in illiterate and low educated Egyptian elderly after modifying the cutoff points to (≤23 in 6-9 years of education, ≤22 1-5 in years of education and ≤21 in illiterate).
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".