Dementia and Mild Cognitive Impairment Identification in Illiterate and Low-Educated People: Systematic Review About the Use of Brief Cognitive Screening Tools
Bibliographic record
Abstract
The rising prevalence of dementia, particularly in low-income and developing countries, highlights the urgent need for effective cognitive screening tools. However, the existing tools often fail to address the unique needs of low-educated and illiterate populations, leading to diagnostic disparities. This review aimed to evaluate cognitive screening tests and domains employed globally to detect mild cognitive impairment (MCI) and dementia in low-educated and illiterate older adults. Following the PRISMA guidelines, Searches were performed in Web of Science, Scopus, and PubMed, targeting studies from January 2000 to 2023 involving adults over 45 years old. Of 1611 studies identified, 27 met the inclusion criteria and underwent pair review. The results revealed that most studies preferred adapting the existing tools to local languages over developing culturally tailored instruments. Twelve cognitive tests specifically designed for low-educated populations were identified, with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) being the most utilized, despite their educational biases. Adjusting the cutoff points improved detection (e.g., MoCA: sensitivity 82.5%, specificity 82%). Notably, the Rowland Universal Dementia Assessment Scale (RUDAS) demonstrated superior performance for low-educated groups (sensitivity 89% and specificity 93%). The findings underscore the critical need for region-specific cognitive batteries that integrate functional assessments, ensuring equitable and accurate diagnosis across diverse educational backgrounds.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".