Evaluating the relationship between education level and cognitive impairment with the <scp>M</scp>ontreal <scp>C</scp>ognitive <scp>A</scp>ssessment Test
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) is defined as 'a cognitive decline greater than that expected for an individual's age and education level but that does not interfere notably with activities of daily life'. The Montreal Cognitive Assessment (MoCA) is a screening test for MCI. METHODS: We investigated the performance of the Turkish version of the MoCA in detecting MCI among elderly persons in a rural area, the majority of whom have a low level of education. We evaluated 50 consecutive men referred from an outpatient clinic. Educational level was divided into three categories: group 1, less than primary (<5 years); group 2, primary (5 years); group 3, more than primary (>5 years). We evaluated the effect of education on MoCA scores and compared subjects' test performance among the different categories of education level. RESULTS: A total of 50 male patients with MCI (mean age: 70.74 ± 7.87) met the inclusion criteria. There were no differences in the total scores based on education or in the subscores for visuospatial/executive function, naming, attention, abstraction and delayed recall. Language was the only domain that showed significant differences between the groups. In post-hoc analysis, differences were found between groups 1 and 3 and between groups 1 and 2. Group 1 had significantly lower scores for language. The repeat subscore for language was significantly lower in group 1 than in group 2. In fluency, there were significant differences between groups 2 and 3 and between group 1 and 3. CONCLUSION: To our knowledge, this is the first study to analyze the applicability of the Turkish version of MoCA in populations with little education. Our results emphasize the need to adapt the language sections of this test, so it can be easily used in populations with low education levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it