Education bias in typical brief cognitive tests used for the detection of dementia in elderly population with low educational level: a critical review
Bibliographic record
Abstract
Dementia is a significant decline in cognition that interfere with independent, daily functioning. Dementia is a syndrome caused by a myriad and include primary neurologic, neuropsychiatric, and medical conditions. It has been projected that the prevalence of dementia will triple in the elderly population by the year 2050. Despite the benefits of early diagnosis, there is an effective under-detection of around 62% of people with mild cognitive impairment (MCI) or dementia. One of the factors associated with this problem is that diagnostic techniques are affected by the educational level of those evaluated. This is an important aspect to consider in the use of brief cognitive tests for the detection of dementia. This review presents and critically analyzes the available evidence regarding the effect of educational level on the diagnostic utility of three of the most widely used tools in the clinical setting: the Mini-mental Test Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Addenbrooke's Cognitive Examination (ACE). Previous evidence shows that the tasks that require reading, writing, calculation, phonological fluency, and visuoconstruction are affected by educational level. These results lead to discourage the use of these tests in older people with less than 6 years of schooling. The development of brief cognitive tests appropriate for people with a low educational level is recommended. We posit that adequate cognitive tests should not consider tasks or items that resemble characteristics of academic contexts and should be more analogous to daily activities situations.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".