Cognitive Assessment Tools for Screening Older Adults With Low Levels of Education: A Critical Review
Bibliographic record
Abstract
Introduction: Cognitive assessment of illiterate or low-educated people is particularly challenging because several battery tasks require a certain educational background. Early detection of mild cognitive impairment (MCI) using validated screening tools in older adults is of great importance since they are the target population group that could benefit from new drugs that are being investigated in clinical trials for the treatment of dementias. The assessment of psychometric properties of cognitive tests are not well established in patients with low education. The present study aimed to critically review the literature on cognitive assessment tools used for screening cognitive syndromes including MCI and AD in older adults with low education. Methods: We searched cross-sectional and prospective studies with adults over 55 years of age through a systematic electronic search of the following databases: MEDLINE, LILACS, Cochrane, and SCOPUS. Results: A significant number of assessment tools available (total of 44), but only a few of them showed diagnostic accuracy for the diagnosis of MCI and AD in adults with low education: Mini-Mental State Exam, Montreal Cognitive Assessment, Persian test of Elderly for Assessment of Cognition and Executive function and The Six Item Screener and Memory Alteration Test. Few studies evaluated adults with low education with range of cutoff scores for various cognitive test batteries. Conclusion We found that a small number of studies evaluated adults with 4 years of education or less. Our findings further support the need for the development of specific tools for the assessment of low-educated adults.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".