Prevalence of Mild Cognitive Impairment and Dementia in Saudi Arabia: A Community-Based Study
Bibliographic record
Abstract
INTRODUCTION: The age of the population in Saudi Arabia is shifting toward elderly, which can lead to an increased risk of mild cognitive impairment (MCI) and dementia. OBJECTIVE: The aim of this study is to determine the prevalence of cognitive impairment (MCI and dementia) among elderly patients in a community-based setting in Riyadh, Saudi Arabia. METHODS: In this cross-sectional study, we included patients aged 60 years and above who were seen in the Family Medicine Clinics affiliated with King Faisal Specialist Hospital and Research Centre. Patients with delirium, active depression, and patients with a history of severe head trauma in the past 3 months were excluded. Patients were interviewed during their regular visit by a trained physician to collect demographic data and to administer the validated Arabic version of the Montreal Cognitive Assessment (MoCA) test. RESULTS: One hundred seventy-one Saudi patients were recruited based on a calculated sample size for the aim of this study. The mean age of included sample was 67 ± 6 years. The prevalence of cognitive impairment was 45%. The prevalence of MCI was 38.6% and the prevalence of dementia was 6.4%. Age, low level of education, hypertension, and cardiovascular disease were risk factors for cognitive impairment. CONCLUSION: Prevalence of MCI and dementia in Saudi Arabia using MoCA were in the upper range compared to developed and developing countries. The high rate of risk factors for cognitive impairment in Saudi Arabia is contributing to this finding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".