Influence of Social and Demographic Factors on the Montreal Cognitive Assessment (MoCA) Test in Rural Population of North-Eastern Greece
Bibliographic record
Abstract
The current study aims to investigate the influence of socio-demographic factors on the Montreal Cognitive Assessment (MoCA) test results in a Greek-speaking population consisting of a sample of healthy older adults, individuals with mild cognitive impairment (MCI), and dementia patients in rural areas. In addition, the current research focuses on determining optimal cut-off scores for the clinical diagnoses of MCI and dementia. The data originated from 283 participants in an ongoing registry of the Neurology Department of Alexandroupolis University Hospital, recruited in different rural districts of north-eastern Greece, across a broad range of educational and occupational categories. Total and sub-domain scores for the MoCA varied significantly, according to sex, age, and education, among the three study groups. The optimal cut-off points of 25/26 for the MoCA total score was determined to classify healthy subjects from individuals with MCI, 24 to discriminate healthy participants from demented, and 21/22 to discriminate subjects with MCI from dementia. Overall, the clinical use of the MoCA test can be supported by demographically adjusted standard scores in a Greek-speaking rural population. These findings serve to improve the diagnostic accuracy and utility of the MoCA test.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".